A recent United States Tax Court case provides another example of a taxpayer who has been stung by using the wrong version of a private delivery service, in this case FedEx “Express Saver,” resulting in the late filing of an important tax document.

Filing Requirements for U.S. Tax Court Petitions

Tax documents generally must be filed by a certain date. One such document is a petition filed by a taxpayer with the United States Tax Court. If a taxpayer wishes to challenge a notice of deficiency (a notice from the Internal Revenue Service (“IRS”) stating that it has determined that the taxpayer owes additional tax), the taxpayer must file a petition in the United States Tax Court within 90 days after the notice of deficiency is mailed by the IRS to the taxpayer. (The period is 150 days if the notice is addressed to a person outside the United States. Saturdays, Sundays, and legal holidays in the District of Columbia are not counted as the last day of the 90-day period.)

The Internal Revenue Code establishes the 90-day (or 150-day) filing requirement. If the Tax Court does not receive the petition within the required time frame, it has no authority to hear the case. This means the taxpayer cannot challenge the IRS determination in court without first paying the tax the IRS says it owes and then suing the government for a refund.

Failing to Timely File

In Lynch v. Commissioner of Internal Revenue, the Tax Court found that the taxpayer failed to timely file a petition because it used FedEx Express Service, and the petition was received and filed by the Tax Court after the 90-day period.^[1] The due date for the petition was January 22, 2024, and the envelope for the petition showed a ship date of January 22, 2024, but the petition was not received and filed by the Tax Court until January 24, 2024, two days after the due date. Accordingly, the court held it could not hear the taxpayer’s case.

Exceptions to the Timely Filing Rule

There are two types of exceptions to the timely filing rule. Under the first exception, a tax document that is sent on or before a due date will be treated as received by the due date if it is actually received. Under the second exception, a tax document that is sent on or before a due date will be treated as received by the due date, whether or not it is actually received, if specific delivery methods are used.

When does the first exception apply?

U.S. regular mail. If a tax document is mailed by regular mail, the tax document is treated as timely delivered, even if it is delivered after the due date, if the following conditions are met:

• The postmark is on or before the due date.
• The tax return and payment are in an envelope and properly addressed.
• The envelope has enough postage.
• The tax document is actually received.

When does the second exception apply?

U.S. registered and certified mail. If a tax return is mailed by registered or certified mail, the mailing will constitute prima facie evidence that the tax document was delivered if the following conditions are met:

• The taxpayer has proof that the tax document was properly registered or that a postmarked certified mail sender’s receipt was properly issued.
• The envelope was properly addressed to the IRS.

Private Delivery Services and IRS Guidelines

The IRS has recognized certain private delivery services as the equivalent of registered and certified mail.^[2] These include specific delivery services offered by DHL Express, FedEx, and UPS. Because the taxpayer in Lynch used FedEx Express Saver, which is neither U.S. mail nor one of the designated private delivery services, the taxpayer could not benefit from the first or second exception.

Other taxpayers have made the same mistake. The danger with DHL Express, FedEx, and UPS is that taxpayers sometimes mistakenly believe that any of the services provided by these private delivery services will satisfy the timely filed requirement if sent on or before the due date. IRS Notice 2016-30, which provides the IRS “Designation of Private Delivery Services,” cautions that “[o]nly the specific delivery services enumerated in this list are designated delivery services . . . DHL Express, FedEx, and UPS are not designated with respect to any type of delivery service not enumerated in this list. Taxpayers are cautioned that merely because a delivery service is provided by DHL Express, FedEx, or UPS, it does not mean that the service is designated for purposes of the timely mailing treated as timely filing/paying rule . . . .”

For another unfortunate case involving an untimely filing, see “Mailing Your Tax Return by the Due Date Is Not Enough.”

The Takeaway for Timely Filing Tax Documents

When filing tax documents, choose the mailing method that best ensures a timely filing. Registered or certified U.S. mail is always a safe choice.

1. No. 1398-24, 2024 BL 133102 (T.C. Apr. 18, 2024). ↑

2. See Private Delivery Services PDS. ↑

On April 3, 2024, the Federal Trade Commission (FTC) voted along party lines (3 to 2) to ban all worker noncompetition provisions. The final rule applies to all employees, including senior executives, and will become effective 120 days after publication in the Federal Register.

The regulation deems employee noncompetes an “unfair method of competition,” pursuant to Section 5 of the FTC Act. The majority believes that noncompete provisions exploit workers and that the federal ban will increase innovation, spur establishment of new businesses, and increase wages.

The two Republican commissioners dissented on the basis that they do not believe the FTC has the legal authority to issue the ban. Although the dissenting commissioners agreed with their Democratic colleagues’ concerns for worker mobility, they explained that only elected representatives have the authority to enact such a sweeping ban.

Within hours of the FTC vote, a tax preparation and software firm announced that it filed a lawsuit challenging the agency’s authority and structure. Separately, the U.S. Chamber of Commerce has reiterated its commitment to challenge the final rule.

Details of the Ban

The final rule applies to any written or oral employment term or policy that penalizes or prevents a worker from (a) seeking or accepting work in the U.S. with a different employer or (b) operating a business in the U.S. after the conclusion of the employment that includes the term or condition. The rule prohibits entering into new noncompete agreements on or after the effective date with any worker. The rule also prohibits enforcing or attempting to enforce a noncompete clause that existed before the effective date for any worker except for those who qualify as senior executives. The ban does not apply to confidentiality agreements or agreements prohibiting solicitation of a former employer’s customers or employees.

Key definitions 

The term “worker” is defined broadly as a natural person who works or worked, including, but not limited to, an independent contractor, extern, intern, volunteer, apprentice, or sole proprietor who provides a service to a person. Although the term “worker” includes individuals who work for a franchisee or franchisor, it does not include a franchisee in the context of a franchisee-franchisor relationship.

“Senior executive” means a worker who (1) held a policy-making position and (2) received total annual or annualized compensation of at least $151,164 in the preceding year, or a portion of the preceding year, prior to the worker’s departure if the worker departed from employment.^[1] Total annual compensation does not include board, lodging, meals, and other facilities as defined in 29 CFR 541.606, and it excludes medical insurance, life insurance, retirement plans, and other similar fringe benefits.

“Policy-making position” means a president, chief executive officer, or any other officer or person with policy-making authority for the business entity. An officer of a subsidiary or affiliate that is part of a common enterprise who has policy-making authority for the common enterprise may also qualify.

“Policy-making authority” is the final authority to make policy decisions that control significant aspects of the company or common enterprise and does not include authority limited to advising or exerting influence over policy decisions or having final authority to make decisions for only a subsidiary of or affiliate of a common enterprise.

Notably, the final rule eliminates the ban on provisions that have the “de facto” effect of a noncompete. It does, however, expressly include in the definition of banned “non-compete clause” an employment term or policy that “penalizes” a worker for seeking or accepting work for a competitor. This addition is intended to capture “forfeiture for competition” clauses in benefit plans and contracts, which allow a worker to move to a competitor but provide that the worker will forfeit equity grants, options, benefits, or contract rights if they compete against the employer.

The FTC specifically stated that nonsolicitation provisions “are generally not non-compete clauses under the final rule because, while they restrict who a worker may contact after they leave their job, they do not by their terms or necessarily in their effect prevent a worker from seeking or accepting other work or starting a business.” The FTC, however, asserts that nonsolicitation agreements “can satisfy the definition of [a] non-compete . . . where they function to prevent a worker from seeking or accepting other work or starting a business after their employment ends.” Whether a nonsolicit has the practical effect of a noncompete is a “fact-specific inquiry.”

Notice requirement

Before the rule’s effective date, employers are required to provide written notice to current or former workers that the worker’s noncompete clause cannot legally be enforced and will not be enforced against the worker. Model (not mandatory) language is provided. The written notice can be delivered by mail, email, or text message.

Impact on State Law and Attorneys General

The federal ban supersedes any inconsistent state laws that are less protective of employees, while leaving intact state laws that provide employees greater protection.

The regulation expressly provides that it shall not be construed as altering, limiting, or affecting the authority of state attorney generals, agencies, or private persons to bring an action arising under any state law or regulation.

Exceptions to the Ban

The final rule provides for certain exceptions.

• Sale of business: The regulation does not apply to a noncompete clause entered into by a person pursuant to the sale of a company, a person’s ownership interest in a business entity, or all or substantially all of a company’s operating assets. The 2023 proposed rule only excepted noncompetes connected to the sale of ownership interests of 25 percent or more.
• Accrued claims: The ban does not apply where a cause of action related to a noncompete clause accrued prior to the effective date.
• Good faith: It is not considered an unfair method of competition to enforce a noncompete or to make representations about a noncompete where a person has a good-faith basis to believe the ban is inapplicable.

The FTC commentary, however, makes clear that the sale of business exception does not apply to “forfeiture for competition” clauses in incentive plans or equity agreements. Accordingly, while such provisions would remain enforceable in existing agreements with senior executives, the FTC rule bars them after the rule’s effective date.

Additionally, although Section 5 of the FTC Act applies only to for-profit entities, the courts apply a fact-sensitive analysis that might muddy the waters for nonprofits with noncompete clauses. Accordingly, nonprofits should tread carefully and consider the use of confidentiality and nondisclosure agreements.

What Happens Next?

Legal challenges to the FTC’s authority are already ongoing, and there is a significant likelihood that the rule will not become effective or will be delayed. Regardless of the pending and future legal challenges, confidentiality and customer and employee nonsolicitation agreements continue to be important tools for employers to protect their valuable information as well as customer and employee relationships. Employers must assess their existing agreements imposing post-employment restrictions, including noncompetition agreements that would be banned under the FTC rule, and confidentiality and nonsolicitation agreements that are not. State law governing these post-employment agreements has recently and dynamically evolved. Legislators in many states have enacted bans or strict limits on these restrictive covenants, and courts have grown far more resistant to enforcing them. Clear and precise drafting is essential, and employers with workers in multiple states must account in their agreements for different state laws.

Employers also need to prepare to provide the required notice under the final rule. The notification must be made by the effective date, 120 days after the rule is officially published. Even though there is a significant chance that the rule will not survive the legal challenges it faces, it could take significant time to identify the workers who are subject to oral or written noncompetes or equivalent employee policies, compile the relevant worker address information, and prepare to issue the notices, if necessary.

1. Total annual compensation may include salary, commissions, nondiscretionary bonuses, and other nondiscretionary compensation earned during that fifty-two-week period. ↑

Editors

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First Circuit

Second Circuit

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Ninth Circuit

Tenth Circuit

Eleventh Circuit

DC Circuit

§ 9.1. Introduction

This year we have seen notable updates to trade secret and employee mobility laws, with the federal government and various states following California’s more restrictive approach to restrictive covenants and other trade secret issues. These changes highlight the ongoing development of trade secret and restrictive covenants laws in response to the continued move towards a globalized market. To that end, 2023 saw updates to trade secret laws addressing key issues ranging from enhanced enforcement mechanisms to clarifications on the scope and limitations of protection afforded to trade secrets and confidential information.

Updates in this area have also worked to clarify the parameters of trade secret protection, particularly concerning the definition of what constitutes a trade secret and necessary measures for maintaining confidentiality. One of the central updates in this area relates to trade secrets and cybersecurity laws, with various circuits and courts recognizing the need to protect against the heightened risks of data breaches and unlawful disclosures of confidential information.

As businesses navigate the increasing complexities of the marketplace, the evolving framework for trade secrets and employee mobility laws provides critical guidance for businesses in safeguarding their highly valuable assets. This Trade Secrets and Employee Mobility chapter provides an overview of the key developments for 2023, highlighting the implications for businesses and practitioners.

§ 9.1.1. First Circuit

Over the last year, the First Circuit has not seen a significant change in how courts have addressed noncompete and trade secret litigation. Since the Federal Trade Commission proposed a rule banning noncompete agreements in January of this year, none of the jurisdictions within the First Circuit have sought to jump ahead of the FTC by introducing new laws to ban noncompete agreements. Recent litigation in this jurisdiction has primarily been state and federal courts deciding what agreements are valid under the current statutory framework.

Below are two examples from Massachusetts, one from federal court and one from state court. The first case is from last year, when the Federal District Court for the District of Massachusetts issued a decision on the validity of two separate noncompete agreements, one signed at the commencement of employment and the other signed mid-employment under the Massachusetts Noncompetition Agreement Act. The second is from the Massachusetts Superior Court, in which the Court issued a decision clarifying sale of business exception under the statute, specifically regarding the sale of client relationships.

Cynosure LLC v. Reveal Lasers LLC, No. CV 22-11176-PBS, 2022 WL 18033055 (D. Mass. Nov. 9, 2022). In this case, the District Court of Massachusetts was tasked with determining whether two separate noncompete agreements with a Delaware choice of law provision were enforceable under the Massachusetts Noncompetition Agreement Act (“MNAA”). The case arose after Cynosure sought a preliminary injunction to enforce noncompete provisions against two former employees. However, the two employees signed the agreements at different points in their employment. The first employee entered into the agreement during his employment in exchange for a stock purchase agreement, while the second employee entered into the agreement at the beginning of his employment. Although the two agreements contained a Delaware choice of law provision, the Court stated that the provision was “valid only if it does not have the primary effect of voiding Massachusetts law.” The MNAA, which governed the agreements, had two specific requirements for an agreement made during employment: (1) “fair and reasonable consideration;” and (2) “mutually agreed upon consideration” or “garden leave.” Since the employee was offered a stock purchase agreement in exchange for signing the noncompete agreement, the Court found that Cynosure had the met the “fair and reasonable consideration” and “mutually agreed upon consideration” requirements along with holding that the agreement satisfied the remaining requirements of the MNAA, thus making it enforceable. As to the second employee, the Court did not have to analyze the sufficiency of consideration, because Cynosure failed to advise the employee of his right to consult counsel as explicitly required by the MNAA when the noncompete agreement is signed at the commencement of employment, thus rendering the agreement unenforceable as to the second employee.

Lighthouse Insurance Agency, Ltd v. Lambert, 2284CV01162, Mass. Super. (June 8, 2022) (unpublished). In Lighthouse, Lambert worked for Lighthouse Insurance Agency as a licensed insurance producer from 2013 to 2021. Prior to October 2020, Lambert’s employment agreement paid him 50 percent commission for new accounts and 40 percent for any renewals by those accounts. In October 2020, Lighthouse offered Lambert a new employment agreement with an $80,000 salary along with 40 percent commission for new accounts. The new employment agreement also contained noncompetition provisions that restricted Lambert from working for a competitor for a year after leaving Lighthouse. Lighthouse also offered to “buy back Lambert’s existing commission rights” to the renewals. Lighthouse characterized the offer as the company buying Lambert’s “book of business” by purchasing his relationship with those clients. Lambert signed the new agreement the day it was offered to him, which took effect immediately. Lighthouse terminated Lambert on July 15, 2021. Lambert then went to work for a competing insurance company in late March 2022. In early April 2022, he received a call from the CEO of a Lighthouse client who told him he would move the company to Lambert’s new employer because he wanted to continue working with Lambert. Lambert did not ask the client to move, but once Lighthouse found out, it sued him. As a baseline issue, the Court found the noncompete agreement to be invalid because it failed the basic requirements of the MNAA, including giving Lambert at least 10 days’ advance notice and notifying Lambert of his right to consult counsel before signing the agreement. The Court then addressed Lighthouse’s argument that the MNAA did not apply because the agreement fell under the sale of business exception. The Court ultimately rejected the argument, holding that the exception did not apply because Lambert did not sell a business to Lighthouse. The Court noted that Lambert did not own the accounts he received commission from and that they belonged solely to Lighthouse. Therefore, Lambert could not sell or assign any interest to those accounts to Lighthouse or anyone else. In the Court’s view, what Lighthouse had actually done was convert Lambert’s continuing right to commission on the renewals into a new right for a fixed salary. Because there was no sale of business, the Court’s original MNAA analysis applied, and the preliminary injunction was denied.

§ 9.1.2. Second Circuit

Over the last year, the First Circuit has not seen a significant change in how courts have addressed noncompete and trade secret litigation. However, there are some notable cases to be aware of, as detailed below.

Employment Mobility (Breach of Duty of Loyalty; Fiduciary Duties)

Onyx Renewable Partners L.P. v. Kao, No. 22-CV-3720 (RA), 2023 WL 405019 (S.D.N.Y. Jan. 25, 2023) (finding the plaintiff adequately pled defendant’s breach of the fiduciary duties of loyalty, care, and good faith under Delaware law because plaintiff plausibly alleged that defendant misappropriated plaintiff’s trade secrets, where defendant’s role as plaintiff’s former general counsel established fiduciary duties because plaintiff was responsible for “critical matters” such as development contract negotiations and financing arrangements, and the plaintiff’s limited partnership agreement did not “clearly and unambiguously express[] the parties’ intent” to limit or eliminate fiduciary duties by disclaiming that defendant should render services “faithfully” and “to the best of his ability,” where defendant allegedly misappropriated plaintiff’s trade secrets by downloading confidential and protected files to an external drive prior to their resignation and later starting a competitor company).

Iacovacci v. Brevet Holdings, LLC, No. 1:18-CV-08048-MKV, 2023 WL 2631966 (S.D.N.Y. Mar. 24, 2023), reconsideration denied, No. 1:18-CV-08048-MKV, 2023 WL 4118086 (S.D.N.Y. June 22, 2023) (denying plaintiff’s motion for summary judgment on breach of covenant counter-claim because a question of fact remained regarding whether plaintiff agreed to be legally bound by the noncompete and confidentiality provisions of their employer’s handbook, where plaintiff acknowledged receipt of handbook language, which stated that the “employee agrees and understands that if they violate these policies, they will be subject to discipline . . . and shall be responsible for all damages sustained by [company] as a result of such violation”).

Restrictive Covenants (Covenants Not to Compete)

Connecticut Public Act No. 23-97, effective October 1, 2023, (1) expands the existing physician noncompete law to restrict noncompete agreements entered into with physician assistants (PA) and advanced practice registered nurses (APRN), and (2) amends Connecticut’s noncompete law for physicians. Like physicians, PAs and APRNs may not be subject to noncompete agreements with a duration of more than one year. See Public Act No. 23-97 § 14(1)(b)(2), 15(1)(b)(1). Restrictions on competition are limited to a fifteen-mile radius from the PA’s or APRN’s primary site of practice as identified in the agreement. See id. Further, PA and APRN agreements are subject to a statutory reasonableness analysis applying a set of factors. See id. § 14(1)(b)(1), 15(1)(b)(1). Any agreement with a physician entered into, amended, extended, or renewed on or after October 1, 2023, will not be enforceable in the event that the physician “does not agree to a proposed material change to the compensation terms” in the contract or agreement “prior to or at the time of the extension or renewal of the contract or agreement”; and “the contract or agreement expires and is not renewed by the employer or the employment or contractual relationship is terminated by the employer, unless such employment or contractual relationship is terminated by the employer for cause.” See id. § 13(b)(3) (exempting practices of thirty-five or fewer physicians the majority ownership of which is comprised of physicians). The parties must identify the physician’s primary practice site in the noncompete agreement and limit the definition of “primary site” to “any singe office, facility, or location where [the] physician practices.” See id. § 13(a)(1).

Customer & Employee Nonsolicitation Agreements

Davis v. Marshall & Sterling, Inc., 217 A.D.3d 1073, 191 N.Y.S.3d 207 (2023) (finding enforceable defendant insurance company’s nonsolicitation and post-termination commission sharing employment provisions enforceable against plaintiff former employees who founded their own insurance company and took nine former clients because, although “generally not favored,” defendant had a “valid business interest to protect” because defendant “solicited, developed, and serviced” those clients such that the clients were the “product of defendant’s efforts, financial expenditures, and goodwill, all of which defendant has a legitimate interest in protecting,” further noting that plaintiffs had neither “any prior experience in the insurance field . . . nor did they have any clients or books of business of their own” when they joined defendant’s insurance agency).

Misappropriation of Trade Secrets

Syntel Sterling Best Shores Mauritius Ltd. v. The TriZetto Grp., Inc., 68 F.4th 792, 804 (2d Cir. 2023), cert. denied sub nom. Trizetto Grp., Inc. v. Syntel Sterling, No. 23-306, 2023 WL 7117087 (U.S. Oct. 30, 2023) (affirming the district court’s denial of plaintiff’s Rule 50(b) motion upon a jury’s finding that plaintiff misappropriated defendant’s trade secrets and rejecting plaintiff’s argument that the deletion of the noncompete provision in the parties’ master services agreement (MSA) authorized plaintiff to use defendant’s trade secrets to compete with defendant, finding that plaintiff was still obligated to abide by the MSA’s confidentiality provisions, and the deletion of the noncompete provision did not “effectively expand[]” the definition of “Services” to authorize plaintiff to leverage defendant’s trade secrets for any services performed for third parties in competition with defendant because the amendment to the noncompete provision identified the definitions impacted and did not identify “Services,” and the MSA separately prohibited plaintiff from “commercially exploit[ing]” any of defendant’s data).

Damages

Syntel Sterling Best Shores Mauritius Ltd. v. The TriZetto Grp., Inc., 68 F.4th 792, 804 (2d Cir. 2023), cert. denied sub nom. Trizetto Grp., Inc. v. Syntel Sterling, No. 23-306, 2023 WL 7117087 (U.S. Oct. 30, 2023) (vacating the district court’s $285 million compensatory damages award for avoided costs under the Defend Trade Secrets Act (DTSA) because the DTSA does not permit recovery of avoided costs, “i.e., the costs a trade secret holder had to spend in research and development that a trade secret misappropriator saves by avoiding development of its own trade secret,” as unjust enrichment where plaintiff’s unjust gain was addressed in computing lost profits for defendant’s actual loss, and defendant suffered no compensable harm beyond that loss, because “those profits were the only enrichment [plaintiff] unjustly gained at [defendant]’s expense,” and plaintiff’s “onetime use of the trade secrets . . . did not jeopardize their continued value” to defendant, as defendant retains their profitable use and a permanent injunction prohibits plaintiff from using them in the future, where “focusing exclusively” on the DTSA’s compensatory damages provision to award avoided costs “ignores the extent to which [plaintiff]’s misappropriation injured [defendant] and impermissibly discounts the comparative appraisal that governs equitable trade secret remedial determinations,” which would distort the DTSA’s remedial scheme by permitting avoided costs awards that “are more punitive than compensatory” and ignore the DTSA’s separate provision for punitive damages).

§ 9.1.3. Third Circuit

Recent Third Circuit cases have revolved around challenging employer’s efforts to protect proprietary information and employing noncompete agreements.

Although no heightened pleading standard governs claims related to proprietary information, courts increasingly dismissed claims for insufficient pleading. In Illumina, Inc. v. Guardant Health, Inc., 2023 U.S. Dist. LEXIS 15865 D. Del. Jan. 31, 2023, the District of Delaware granted defendant’s motion to dismiss all but one claim because the complaint lacked sufficient factual support for its allegations, including trade secret misappropriation, because plaintiffs failed to both identify the trade secrets within 51,000 emails allegedly misappropriated and distinguish which emails contained trade secrets. Conversely, plaintiffs identified both the specific documents purportedly containing trade secrets and protected content at issue related to the lone misappropriation claim that survived. Similarly, in IQVIA, Inc. v. Breskin, 2023 U.S. Dist. LEXIS 47174 (E.D. Pa. Mar. 20, 2023), the Eastern District of Pennsylvania dismissed plaintiff’s misappropriation claims for failure to identify the trade secrets claimed within some 10,000 documents or to distinguish trade secrets from confidential information.

Conversely, providing too much information in initial pleadings may preclude success on a request for preliminary injunction. In JRM Construction Management, Inc. v. Plescia, 2023 U.S. Dist. LEXIS 59380 (D.N.J. Apr. 4, 2023), the District of New Jersey found defendant’s denials and alternate explanations underscored significant disputes of material fact that undermined its ability to show reasonable likelihood of success on the merits and irreparable harm for purposes of the TRO, preliminary injunction, and request for expedited discovery.

The Delaware Chancery Court has recently made a number of significant noncompete decisions. First, in Kodiak Building Partners, LLC v. Adams, 2022 WL 5240507 (Del. Ch. Oct. 6, 2022), the court nullified an overly broad noncompete arising out of the sale of a business, finding the agreement unlawfully restricted competition with any business in the purchasing company’s portfolio, not just the business of the purchased company. The Kodiak court also refused to revise the noncompete, notwithstanding Delaware law permitting courts to reform overly broad noncompetes and the language of the agreement explicitly permitted the court to revise the agreement. Id. at n.49. Second, in Ainslie v. Cantor Fitzgerald, L.P., 2023 WL 106924 (Del. Ch. Jan. 4, 2023), the same court invalidated a noncompete and a forfeiture-for-competition provision in a partnership agreement. Just like in the Kodiak case, the Ainslie court refused to revise the agreement.^[1] Third, in Intertek Testing Services NA, Inc. v. Eastman, 2023 WL 2544236 (Del. Ch. March 16, 2023), the court refused to enforce or revise a noncompete accompanying the sale of a business when the geographic scope extended worldwide, which includes areas where the previous employer did not conduct business. Finally, in Frontline Technologies Parent LLC et al. v. Murphy, 2023 WL 5424802 (Del. Ch. Aug. 23, 2023), the court refused to enforce or revise a noncompete that prohibited the employees from working for a competitor of the holding company when the employees worked for a competitor of the operating subsidiary.

§ 9.1.4. Fourth Circuit

Much like the rest of the country, the Fourth Circuit has seen new laws that affect noncompete agreements. In Maryland, employers are now prohibited from entering into noncompete agreements with low-wage workers, who earn 150 percent of the minimum wage. Maryland recently enacted the Fair Wage Act of 2023, effective January 1, 2024, which increases the minimum wage from $13.25 to $15 per hour. Under Senate Bill 591, the salary threshold for noncompete agreements will increase from $19.88 to $22.50 per hour, for an annual income of $41,350 in 2023 or $46,800 in 2024.

Additionally, the Fourth Circuit has authored new jurisprudence impacting trade secret litigation. The Court’s decision in Synopsys, Inc. v. Risk Based Sec., Inc., No. 22-1812, 2023 WL 4009505 (4th Cir. June 15, 2023), highlights that trade-secret plaintiffs must prove that their secret information is not just commercially valuable, but that it has independent economic value because it is a secret. Risk Based Security Inc. (RBS) and Synopsys Inc. (Synopsys) were both companies involved in identifying vulnerabilities in open-source software. RBS sued Synopsys, alleging that it misappropriated RBS’s trade secrets in a database RBS compiled and licensed to other companies. RBS claimed that Synopsys used the licensed database to develop its own database, violating the license agreement and misappropriating seventy-five RBS trade secrets. The court held that a general showing of a company’s acquisition price and licensing revenue, without a nexus to the asserted trade secret, was not evidence of the asserted trade secrets’ independent economic value. Moreover, the court found that the evidence did not satisfy the statutory definition of a trade secret under both Virginia and federal law. The decision emphasizes that companies cannot show that a trade secret derives its value from being kept secret, but rather need to demonstrate economic evidence tied to the trade secret’s value. Further, employers should take reasonable efforts to maintain secrecy of such trade secrets to meet the definition of a trade secret.

§ 9.1.5. Fifth Circuit

The Fifth Circuit has seen a few cases that practitioners ought to be aware of, however we have not seen any new laws within the last year impacting trade secrets or related claims.

Joe Formicola v. Virtual Integrated Analytics Solutions, LLC, 14th Court of Appeals, Texas, Case No. 14-22-00412-CV (overturning trial court’s ruling finding that even when defendant admitted he received misappropriated trade secrets, plaintiff’s claim will be dismissed for lack of jurisdiction).

Teligistics Inc. v. Advanced Personal Computing Inc. et al., No. 2019-15000, in the 190th District Court of Harris County, Texas (jury verdict finding that (1) affixing a confidentiality label to a document does not necessarily make the information within a trade secret, and (2) it is acting in bad faith when one improperly alleges misappropriation of trade secrets (i.e., when there is no true confidentiality)).

§ 9.1.6. Sixth Circuit

Over the past year, the Sixth Circuit has seen slight changes in state noncompete law and some high-profile cases related to both noncompete laws and trade secret misappropriation.

Kentucky recently amended Kentucky Revised Statute Section 216.724, which places restrictions on contracts that health care services agencies (“HSA”) can have with their staff. The amendments, effective June 28, 2023, specify that HSAs are restricted in their contractual relations with temporary direct care staff, rather than direct care staff generally. Thus, HSAs cannot restrict temporary direct care staff’s employment opportunities, nor require the payment of damages should a temporary direct care staff member be hired permanently by an employer that has contracted the staff member’s work except under certain circumstances.

An Ohio court also recently considered whether information otherwise accessible to the public qualified as a “trade secret” under the Ohio Uniform Trade Secrets Act (“OUTSA”) in Hanneman Fam. Funeral Home & Crematorium v. Orians, 2023-Ohio-3687 (Ohio 2023). There, plaintiff brought action against its former director and his current employer alleging misappropriation of trade secrets, tortious interference, and conversion based on the former director copying the plaintiff’s customer information before leaving to work for his current employer. On appeal, the Supreme Court of Ohio affirmed summary judgment for defendants, holding that information about customers who had pre-need funeral contracts was not a “trade secret” protected by the OUTSA. Id. at *4. The Court opined that the customer information at issue was not “kept secret” as it was accessible to various employees, provided to third parties, and was available as a public record if requested from the state. Id. at *3. Further, the Court affirmed the holding that the OUTSA preempted plaintiff’s tortious interference and conversion claims as they were based solely on the unauthorized use of its information as alleged in its misappropriation-of-trade-secrets claim. Id. at *4.

In United States v. Xiaorong You, 74 F.4th 378 (6th Cir. 2023), the Sixth Circuit upheld the conviction of a chemical engineer found guilty of conspiracy to commit theft of trade secrets, possession of stolen trade secrets, wire fraud, conspiracy to commit economic espionage, and economic espionage. You, a former employee of Coca-Cola and the Eastman Chemical Co. of Tennessee, stole trade secrets related to BPA-free aluminum can liners with the intent of creating a new company in China that would manufacture the BPA-free chemical. Id. at 384–385. You appealed her conviction, arguing that the jury instructions misstated the intent requirements for trade secret theft, among other claims. Id. at 387. Specifically, You claims the instructions were insufficient because they “failed to require proof that she knew that (1) the owners had taken ‘reasonable measures’ to protect the information; and (2) the information was valuable, in part, because it was secret.” Id. at 391. The Sixth Circuit rejected You’s claims under a plain error review, as the alleged error in the jury instructions did not affect You’s substantial rights. You knew of the measures taken to protect the trade secrets as she certified she did not keep any confidential information in her severance agreement with Coca-Cola. Id. at 392. Further, You knew the information was valuable because she emphasized the value of the BPA-free chemical in her application for a national grant through China’s Thousand Talents program. Id. The Court reached a similar conclusion under de novo review of the jury instructions. Id. at 393.

§ 9.1.7. Seventh Circuit

Over the past year, the Seventh Circuit has seen developing jurisprudence construing Illinois statutory noncompete, nonsolicitation, and trade secrets provisions, and the federal Defend Trade Secrets Act (“DTSA”), rulings on the sufficiency of pleading trade secret misappropriation and tortious interference claims, and a new Indiana noncompete statute.

The Illinois Freedom to Work Act, effective January 1, 2022, applies to agreements entered into on or after that date, rendering void and unenforceable certain restrictive covenants unless minimum annual employee compensation thresholds are met—in excess of $75,000 for noncompetes and in excess of $45,000 for nonsolicits. Courts this past year have held that this statute does not have retroactive effect; thus, it is inapplicable to conduct or agreements entered into before January 1, 2022. In addition, courts have held mere compliance with the minimum pay requirement does not establish per se enforceable restrictive covenants. They still must satisfy common-law principles (codified in another state statute at 820 Ill. Comp. Stat. Ann. 90/15), e.g., employees must receive adequate consideration, the covenant must be ancillary to a valid employment relationship, etc.

Federal district courts in the Seventh Circuit also have rejected the contention that under each of the Illinois Trade Secrets Act (“ITSA”) and DTSA, a written confidentiality agreement must exist in order to establish that a trade secret exists. These decisions recognize employers can maintain secrecy via other steps, like physically securing facilities and limiting employee access to a need-to-know basis. Yet, as the Illinois Court of Appeal held this year, employers must take affirmative measures to prevent others from acquiring or using information for it to qualify as a trade secret under the ITSA. In Total Staffing Solutions, Inc. v. Staffing, Inc., 2023 IL App (1st) 220533 (Ill. Ct. App. June 23, 2023), plaintiff brought a misappropriation of trade secrets claim under the ITSA against a competitor firm created by former employees. Evidence showed that the plaintiff had freely provided to one of its customers, upon request, the information that the plaintiff asserted in the litigation was a trade secret, consisting of its employee names, pay rates, and bill rates. Id. at 19. The Court ruled that when the plaintiff “did not treat the information as confidential and secret,” it was not a protected trade secret. Id. at 19–21.

With regard to the sufficiency of pleading claims under the DTSA, several district courts held it is enough that the complaint alleges broad categories of information without identifying a particular trade secret, so long as facts are alleged that support a plausible inference that the company derives economic value from the information not being generally known or ascertainable.

In terms of new state statutes, Indiana enacted a new noncompete law related to primary care physicians and to physicians in Indiana Code Section 25-22.5-5.5-1 through Section 25-22.5-5.5-4. Effective July 1, 2023, this law bans any noncompete agreements entered into on or after July 1, 2023, between an employer and (specifically) a primary care physician (i.e., family, general pediatric, or internal medicine). The new law also states noncompete agreements between a physician and employer entered into on or after July 1, 2023, are unenforceable if the employer ends the employment without cause, the physician ends the employment for cause, or both parties have fulfilled their respective obligations when the employment contract expires. Also, for noncompetes entered into on or after July 1, 2023, if physicians elect to exercise a contract buyout option, the new law requires good faith negotiations to determine a reasonable purchase price, and specifies a mediation process for this purpose if needed.

Last, pending before the United States Supreme Court is a petition for a writ of certiorari filed by McDonald’s USA, LLC, seeking review of whether “no-poach” clauses previously (but no longer) used in its franchise agreements violated the federal Sherman Antitrust Act. The case, Deslandes v. McDonald’s USA, LLC, is a class-action antitrust suit filed in 2017 by a manager who had to decline a higher-paying job at another McDonald’s franchise due to contract terms prohibiting franchise operators from hiring one another’s employees for six months post-employment. The claim was rejected by the district court and appealed to the Seventh Circuit, where the Federal Trade Commission and U.S. Department of Justice jointly filed an amicus brief in support of the workers, and where the Seventh Circuit vacated the judgment and remanded the case. Deslandes v. McDonald’s USA, LLC, 81 F. 4th 699 (7th Cir. 2023).

§ 9.1.8. Eighth Circuit

Over the past year, the Eighth Circuit has seen several new laws implicating employee mobility and restrictive covenants.

In Iowa, the Legislature adopted new legislation aimed at limiting noncompete agreements for licensed mental health professionals. The law prohibits agreements with “licensed mental health professional[s]” that limits the location in which the licensee may practice, prohibits the licensee from contacting a person previously treated by the licensee, or imposes a time restriction on the practice of the licensee. The law applies retroactively.

In Minnesota, a new law bans noncompetes entered into on or after July 1, 2023. The ban expressly does not apply to nondisclosure, confidentiality, trade secret, or nonsolicit agreements. The law also prohibits, as a condition of employment, an employer from requiring an employee to a choice of law provision if the employee primarily resides in Minnesota. Lastly, the scope of the law extends to include certain independent contractors.

Bucking the trend of limits on restrictive covenant agreements, Missouri specified that specified restrictive covenant agreements between entities and owners are enforceable under certain circumstances. Specifically, the law applies to agreements which “promis[e] not to solicit, recruit, hire, induce, persuade, encourage, or otherwise interfere with, directly or indirectly, employees or owners of a business entity.” Moreover, the law directs courts to modify restrictive covenants which are “overbroad, overlong, or otherwise not reasonably necessary to protect the protectable business interests of the business entity seeking enforcement[.]” However, the law expressly does not affect noncompetes, nondisclosure, or confidentiality agreements or any agreement impacting the owner’s ability to seek or accept employment with another business entity upon termination of the owner’s relationship with a business entity.

In South Dakota, a new law limits post-employment restrictive covenants on healthcare professionals. The law provides that a contract provision entered into on or after July 1, 2023, is voidable if it restricts a healthcare professional, as defined by law, from “practicing or otherwise providing professional services in accordance with the applicable scope of practice, after the conclusion of the practitioner’s employment or after the dissolution of a partnership or other form of professional relationship.”

§ 9.1.9. Ninth Circuit

Over the past year, the Ninth Circuit has seen few developments regarding employee mobility, restrictive covenants, and trade secrets. Most case discussions have concerned the arbitrability of these types of claims. Courts in this Circuit consistently apply the Supreme Court’s heightened standard of requiring “clear and unmistakable” evidence of an agreement to arbitrate arbitrability. And cases have held that the incorporation of the rules of certain arbitral forums, like the AAA rules, constitutes clear and unmistakable evidence of such, but it is not issue specific. See Maguire Insurance Agency, Inc. v. Amynta Agency, Inc., 652 F.Supp.3d 1313, 1321 (W.D. Wash. 2023).

That being said, there were some notable decisions. For instance, in February 2023, the Ninth Circuit issued its decision in Chamber of Commerce of the United States v. Bonta, 62 F.4th 473 (9th Cir. 2023) which held that the Federal Arbitration Act (FAA) preempts California Assembly Bill 51. That bill attempted to protect employees by making it a misdemeanor for an employer to require an employee to sign an arbitration agreement as a condition of employment. There, in 2019, several trade associations sought a declaration that AB 51 was preempted by the FAA, a permanent injunction prohibiting California officials from enforcing it, and a temporary restraining order. Id. at 480–81. The Ninth Circuit affirmed the district court’s ruling granting the plaintiffs’ motion for preliminary injunction and, that the FAA’s preemptive scope is not limited and, therefore, Bill 51’s imposition of civil and criminal penalties is an “unacceptable obstacle to the accomplishment and execution of the full purpose and objects of Congress in enacting the FAA.” Id. at 481, 483, 489.

Cases that came down this year also reiterated that there remains a divide between the states within the Ninth Circuit about whether noncompete agreements are void or enforceable. Kibble & Prentice Holding Company v. Tilleman, 643 F.Supp.3d 1123 (2022) is instructive. There, the District Court of Idaho held that a noncompete agreement was enforceable because the restrained party was a “key employee” based on the expertise and experience he gained while working with his former employer to garner client relationships. Id. at 1134. The time restraint in the agreement was reasonable even though it exceeded the eighteen-month statutory limit in Idaho because there was consideration given for the extra months. Id. at 1136. Idaho and Oregon laws agree that goodwill developed by an employee belongs to the employer and the employer is entitled to protect itself. Id. at 1137.

By contrast, California recently enacted legislation to solidify its stance on the unenforceability of noncompete agreements with new laws that are set to take effect in 2024. In September 2023, Governor Gavin Newsom signed Senate Bill 699, which prohibits employers from entering or attempting to enforce noncompete agreements and establishes that such agreements are void in California.^[2] The law states that these contracts are void in California regardless of where and when the contract is signed. This will prohibit employers outside of California from attempting to prevent former employees from being hired in California. The law also creates new enforcement rights for employees by enabling them to bring private actions to enforce the law and allows the prevailing plaintiff to recover injunctive relief or actual damages, or both. This will deviate from prior trends of these claims being litigated as declaratory relief actions by raising the risk that employers may face. SB 699 will be codified as Section 16600.5 of the California Business and Professions Code and will go into effect on January 1, 2024.

Finally, in October 2023, Governor Newsom signed Assembly Bill 1076, which will work together with SB 699 to further reinforce California’s ban on noncompete agreements.^[3] It will codify a 2008 California Supreme Court decision in Edwards v. Arthur Anderson LLP (2008) 44 Cal.4th 937, in which the Court held that noncompete agreements are void under section 16600 of the California Business and Professions Code no matter how narrowly tailored they are. Id. at 955. AB 1076 will also require employers to notify all employees who were employed after January 1, 2022, in writing by February 14, 2024, that any noncompete agreements they may have signed are void. AB 1076 will be reflected in an amended section 16600 of the California Business and Professions Code and will go into effect on January 1, 2024.^[4]

§ 9.1.10. Tenth Circuit

The Tenth Circuit has not seen any new laws within the last year impacting trade secrets or related claims. However, courts in the Tenth Circuit added to current jurisprudence: Goode v. Zavodnick, No. 120CV00742DDDKLM, 2023 WL 3568126 (D. Colo. Feb. 17, 2023). Here, plaintiff voluntarily shared dream journal records with defendant, knowing that the defendant would share the records with others, and without knowing they possibly constituted a trade secret. Plaintiff later alleged that defendant misappropriated her dream journal records and on that basis, filed a claim for federal and state trade secrets claims. However, those claims were dismissed because she did not sufficiently allege that she took reasonable measures to maintain the secrecy of her dream journal records. Plaintiff attempted to argue that she did not know her dream journal records were trade secrets until it was too late—when she realized her records constituted legitimate dream research. In dismissing plaintiff’s claims, the court held that trade secret protection is not retroactive, and on that basis, dismissed her claims.

Cayo, Inc. v. Swiss Reinsurance Am. Corp., No. 23-CV-00105-MEH, 2023 WL 4744196, at *14 (D. Colo. May 2, 2023). Plaintiff developed “one of the first web-based ‘instant-issue’ life insurance platforms in the country”—“the ‘Instalife’ platform (the ‘Platform’).” Its “Platform provided a safe and secure way for partnering insurance companies to provide life insurance policies directly to qualified applicants with competitively-priced [sic] premiums that could be approved instantly online.” Plaintiff partnered with defendant to optimize the website and its services in order to generate more business. The relationship did not last, and defendant attempted to terminate the agreement. Plaintiff accused the defendant of creating its own website with a very similar platform for the issuance of life insurance policies as plaintiff’s website.

Plaintiff alleged multiple causes of action, including a claim for violation of Colorado’s Uniform Trade Secrets Act (“CUTSA”). The Court dismissed the claim, finding that plaintiff failed to establish that it possessed a valid trade secret. The Court acknowledged that Colorado does not have a heightened pleading standard for misappropriation of trade secrets claims. Even then, the Court found that plaintiffs’ allegation that it provided defendant “access to [the website’s] confidential and proprietary intellectual property, including the coding, design and processes” was insufficient, as it did not provide sufficient factual basis to establish what the confidential and proprietary intellectual property actually were. The Court further found that plaintiff had not plead sufficient facts establishing that it made reasonable efforts to maintain the secrecy of its trade secrets. Plaintiff stated that “took steps to conceal its intellectual property from persons and entities other than Defendants,” but the Court found it lacks any factual support. In sum, even though Colorado has a lower pleading requirement for its Uniform Trade Secrets Act, plaintiffs are still required to set forth facts sufficient to establish each element.

§ 9.1.11. Eleventh Circuit

The Eleventh Circuit has seen various cases that further impact restrictive covenants and other provisions in agreements among parties. For instance, in American Plumbing Professionals, Inc. v. ServeStar, LLC, 2022 WL 628664 (March 4, 2022), a plumbing company filed suit against its former employees and a competing plumbing company, claiming that the individuals were violating their noncompetes and that the competitor had engaged in tortious interference by inducing the individuals to violate their noncompetes. The noncompetes included a geographic restriction for the territory where the employees provided services on behalf of the employer during the last twelve months of their employment. The agreement also included an acknowledgement that “territory” where the employees provided services extended to parts of the United States where the employer transacts business, in light of the company-wide nature of the confidential information and business relationships developed and maintained by the employees. The trial court granted summary judgment to the new employer on the tortious interference claim based on its finding that the vague and defective territory in the noncompetes rendered them unenforceable. The appellate court reasoned that under Georgia’s Restrictive Covenant Act (RCA), a geographic restriction can be enforced even if the employee cannot determine its maximum reasonable scope until the end of his or her employment. The Court found that the geographic restriction in the noncompetes complied with the language that it specifically sanctioned by the RCA and gave the employees fair notice of the maximum reasonable scope of their restraints. This continues to make clear that Georgia trial courts should follow the parameters of Georgia’s RCA when determining whether noncompetes are valid or enforceable.

Another example is North American Senior Benefits, LLC v. Wimmer, 2023 WL 3963931 (Ga. App. June 13, 2023), where two insurance agents left their employer and started a competing business. The court found that two insurance agents’ restrictive covenants, which included employee non-recruitment covenants, were invalid. In the first Georgia appellate decision on this issue of whether a territory was required for this type of restrictive covenant, the Georgia Court of Appeals agreed, holding that non-recruitment and no-hire covenants must have a territory to be enforceable under Georgia law. On the other hand, Georgia’s statute expressly states that customer nonsolicits and nondisclosure covenants are not required to have a geographic scope. The Georgia Court of Appeals also held that courts can “blue-pencil” overbroad covenants by striking offending language, but cannot write in terms where none exist (e.g., courts cannot write in a territory in the agreement where no territory is present). Employers should review and revise restrictive covenant agreements to avoid challenges to the enforceability of an employee nonsolicit and consider whether this means that other restrictive covenants, such as referral source, vendor, and supplier nonsolicits, may also require a territory to be enforceable.

Another case to note is the Eleventh Circuit’s opinion in SIS LLC v. Stoneridge Software Inc. et al., Case Number 21-13567 (11th Cir. 2023), where the Eleventh Circuit upheld the trial court’s rejection of liquidated damages in a trade secret case. This case involved two technology companies who had previously attempted to work together and entered into a confidentiality agreement. The party alleged that defendant had breached the parties’ confidentiality agreement and filed suit to recover damages based solely on the liquidated damages provision in the confidentiality agreement. The Eleventh Circuit, in declining to enforce the liquidated damages provision, reasoned that the formula used for the liquidated damages provision was “not a reasonable method for approximating the probable loss because it is based entirely on the breaching party’s profits, and not on the injury suffered by the non-breaching party.” The Eleventh Circuit concluded that this amounted to an improper penalty under Georgia law and is unenforceable. Employers should review their liquidated damages provisions to ensure it will hold up in a trade secrets dispute.

§ 9.1.12. DC Circuit

2023 was a quiet year for employee mobility opinions in the Washington D.C. area courts. There were no published cases applying or interpreting Washington D.C.’s scaled back noncompete ban. However, the District’s federal court issued a handful of notable opinions on issues that frequently arise in trade secret and customer nonsolicit litigation.

Clevinger v. Advocacy Holdings, Inc., 2023 U.S. Dist. Lexis 121860 (D.D.C. July 15, 2023) provides a thorough analysis of a cornerstone element of injunctive relief—irreparable harm. There, Advocacy’s former CEO resigned to join a competing company and took its customer contact information. Clevinger allegedly then solicited Advocacy’s customers and told some of its customers that Advocacy was “closing” or “pivoting” away from its existing business. Advocacy sought a preliminary injunction to enforce Clevinger’s twelve-month customer nonsolicit agreement. Despite observing that the allegations may represent “stunning breaches” of Clevinger’s fiduciary and contractual obligations, the court denied Advocacy’s motion based on an absence of irreparable harm. The court appeared most persuaded by the fact that Advocacy was able to calculate its damages, and that its business was “about the same” and is “not going out of business anytime soon.”

In Meyer Grp v. Rayborn, 2023 BL 387314 (D.D.C. Sept. 23, 2023) the district court summarily adjudicated several issues that are frequently litigated in cases arising from theft of a customer list. When Rayborn left real commercial restate brokerage Meyer Group, he took handwritten index cards containing customer information. On Meyer’s motion for summary judgment, the court found that Rayborn breached his contractual confidently obligations to Meyer. While the court left the issue of trade secret misappropriation to the jury, it found that the cards were trade secrets. In doing so, the court rejected Rayborn’s argument that some of the cards’ information was readily accessible on the Internet and subscription databases. Key to the court’s decision were its findings that it was not dispositive if “some of the information” written on the cards was publicly available; that lease expiration dates, key contact information, and notes about client preferences were on the cards; and even if individual cards were not trade secrets, a jury could find that the collection of cards gave Meyer Group a “significant competitive advantage” in the real estate market.

When trade secret misappropriation occurs outside of a plaintiff’s view, courts will often decide if the complaint alleges sufficient circumstantial evidence to create an inference of misappropriation. In Aristotle Int’l v. Acuant, Inc., 2023 WL 1469038 (D.D.C. Jan. 4, 2023), Aristotle alleged that its reseller, Acuant, disclosed its trade secrets to a third party, GB Group, during M&A due diligence. In denying Acuant’s motion to dismiss, the court found that Aristotle alleged sufficient circumstantial evidence to plausibly allege the existence of a trade secret and misappropriation. Key to the court’s finding were the complaint’s allegations that Acuant was acquired by GB group three months before Acuant received the alleged trade secret information from Aristotle, and that GB Group quoted from the confidential reseller contract between Aristotle and Acuant. The court declined to require allegations showing direct proof of misappropriation.

1. The Supreme Court of Delaware took this case on appeal, but as of publication, the Supreme Court has not yet rendered a decision. ↑

2. S.B. 699, 2023–2024, Reg. Sess. (Cal. 2023). ↑

3. Clifford R. Atlas et al., New California Law Makes Non-Compete Agreements Unlawful, Not Just Void, California Workplace Law Blog, Oct. 14, 2023. ↑

4. See 2023 Cal. Legis. Serv. Ch. 828 (A.B. 1076) (WEST). ↑

Recent appraisal actions before the Delaware Court of Chancery highlight the need for clarity about the assumptions—both explicit and implicit—in the terminal value component of discounted cash flow (“DCF”) models used for the valuation of companies. Terminal value is the lump-sum discounted value of all cash flows expected to occur after the explicit forecast period.

Consistent with basic economic and finance principles, the terminal value should increase with growth only if the firm can earn a return on capital in excess of its cost of capital (i.e., positive net present value projects). Because such opportunities over the longer term are limited in a competitive market, valuation models that are highly sensitive to the assumed growth rate should be treated with caution, as they likely embed unrealistically optimistic assumptions about value creation.

In an October 2022 decision in Ramcell, Inc. v. Alltel Corp., the court determined the fair value of Jackson Cellular Telephone Co. (“Jackson”) when it was acquired by Alltel Corporation on April 4, 2019.^[1] In its ruling, the court calculated Jackson’s terminal value using the so-called convergence model,^[2] which explicitly links growth, investment, and return on investment. The court reasoned that although Jackson was likely to experience continued growth in the long term, “[t]here is no free growth, and, in this case, the court finds that the terminal value model should make this concept explicit.”^[3]

Ramcell was certainly not the first time that valuation experts have presented the convergence model in the Delaware Court of Chancery. The model has been used in multiple appraisal actions over the past decade, and we find it used in cases dating back to 1990.^[4] However, the Court of Chancery has not universally accepted the convergence model. For example, the recent revised ruling in HBK Master Fund v. Pivotal Software, Inc. rejected the convergence approach, finding that it inappropriately “implemented an effective 0% perpetuity growth rate in the terminal period” when a 2.5 percent growth rate was warranted.^[5]

In this article, we discuss the convergence approach and its implications for growth assumptions in DCF models.

Background on Terminal Value Calculations

DCF models calculate the present value sum of the expected future cash flows of a business. Experts preparing DCF models typically start by examining financial projections from company management or third parties. Company projections often extend only a few years into the future, however. To capture the value of cash flows beyond the projection period, experts estimate a terminal value that reflects the lump-sum equivalent of all future cash flows after the period for which actual projections exist. In many DCF models, a significant portion of the subject firm’s estimated value is attributable to the terminal value.^[6] Embedded within the terminal value are explicit or implicit assumptions about the company’s future profits, growth, risk, and investment needs. These assumptions must be internally consistent. In particular, the investment rate—the fraction of after-tax profits that the company reinvests—will depend on the profitability and rate of growth.

One popular technique for estimating the terminal value involves extrapolating future cash flows from the final year of the projections at a constant long-term growth rate. Under this approach, the subject business’s profit margins, tax rate, and investment rate in the final year of the projections are assumed to remain constant in perpetuity. The simplicity of the approach has great appeal, and, unsurprisingly, its use is widespread. A 2006 survey of corporate financial advisers and private equity professionals, for example, found that 80 percent of participants used the extrapolation approach to estimate terminal value.^[7] The extrapolation approach is also prominent in popular reference books for investment banking professionals.^[8]

Despite its popularity, the extrapolation approach is problematic when there is a mismatch between the projected investment rate and the long-term constant growth rate chosen by the expert.^[9] As the court correctly observed in Ramcell, growth is not free. Instead, growth requires investment, and competition will tend to prevent continued returns in excess of the cost of capital. Thus, low investment but high growth yields an implausibly high return on investment. In Ramcell, for example, a terminal value calculation prepared by the petitioners’ expert using the extrapolation approach implied a return on investment ranging from 193 percent to 227 percent—more than ten times what is plausible in a competitive industry.^[10] The court credited an illustration of this fact prepared by the respondent’s expert in electing to adopt the convergence approach instead.^[11]

The Convergence Approach

The convergence approach, in contrast, explicitly constrains the subject firm’s long-run return on investment to a reasonable target rate. Often, a reasonable return on investment is the firm’s cost of capital—that is, the return that investors expect after paying for all the costs associated with operating the business.^[12] This is because a business consistently earning returns that exceed its cost of capital (thus increasing the value of the business) will tend to attract vigorous competition. Competition, in turn, will exert downward pressure on the company’s return on investment.^[13] For example, a competitor may need only reduce its prices a bit to steal market share and capture some of the “spread” between returns and capital costs in the industry. Outsized profits will also attract new entrants. As a result, excess returns—the spread between return on investment and the cost of capital—should disappear in the long run for competitive industries.

While the convergence approach assumes that a company’s excess returns will (slowly) dissipate, growth in revenue and profits is another matter. Under the convergence approach, a company may continue growing even while its excess returns disappear. The subtlety arises from the distinction between growth and value creation. When a company’s return on investment equals its cost of capital, growth does not create extra value because the cost of funding that growth just offsets the benefit. Two simple examples are investments in projects with zero net present value and an acquisition at a price that reflects the full value of the target. It is only when a company’s return on investment exceeds its cost of capital that growth creates value. On the other hand, growth reduces value when the cost of capital exceeds the return on investment. Thus, while the formula for the convergence model calculates terminal value as if the firm has no growth, it is more precise to say that the firm is growing but the cost of funding that growth offsets the benefit.^[14]

Every Terminal Value Calculation Embeds an Assumption about Long-Term Investment

Careful readers may object that tying the long-term return on investment to the cost of capital requires projecting a firm’s investment expenditures far into the future. In the Pivotal decision, for example, the court inferred that “[t]rying to ascertain a plowback ratio a decade from the valuation date appears speculative at best, at least under these facts.”^[15] While projecting the plowback ratio a decade out may appear speculative, other techniques for calculating terminal value also implicitly embed an assumed plowback ratio.

Consider, for example, the extrapolation approach discussed above. The extrapolation approach grows projected cash flows at a constant rate in perpetuity. Cash flow equals after-tax profits less investment, by definition, so the extrapolation approach inherently includes a specific, constant-growth projection of investment far into the future. The distinction is not that one terminal value calculation approach requires projecting investment and the other does not. By virtue of what the terminal value represents—the present value of a stream of cash flows—both terminal value approaches embed projections of long-term investment. Instead, what distinguishes these approaches is the nature of the specific investment projection and what it implies for other variables of interest, like return on investment. Under the convergence approach, the investment projection imposes economic discipline arising from competitive market conditions in setting the long-term return on investment in relation to the cost of capital. That modeling structure avoids unrealistic scenarios that often arise under the extrapolation approach, where investment returns significantly exceed the cost of capital forever, giving rise to an inflated valuation that is inconsistent with a competitive market.

Conclusion

Growing recognition of the merits of the convergence approach in valuation practice and case law puts the assumptions embedded in alternative approaches in sharp relief. Experts adopting alternative approaches, such as the extrapolation approach, are likely to face heightened scrutiny about whether the often-implicit assumptions embedded in their terminal value calculations are consistent with reasonable expectations for long-run market conditions. On the other hand, experts adopting the convergence approach may face challenges about the competitive forces driving up long-run investment requirements relative to management’s near-term expectations as reflected in company projections.

Disclosure: Michael Cliff was a member of the Analysis Group teams that supported valuation experts for the respondents in the PetSmart, Solera, and Jarden cases cited in the endnotes. Joseph Maloney was a member of the Analysis Group teams that supported valuation experts for the respondents in the PetSmart and Jarden cases. The opinions expressed are those of the authors and do not necessarily reflect the views of Analysis Group or its clients.

1. Ramcell, Inc. v. Alltel Corp., No. 2019-0601-PAF, 2022 WL 16549259, at *1 (Del. Ch. Oct. 31, 2022). ↑

2. Id. at *27–28. This model is also referred to as the “plowback,” “value-driver,” or “McKinsey” model. ↑

3. Id. at *28. ↑

4. See, e.g., Cede & Co. v. Technicolor, No. 7129, 1990 Del. Ch. LEXIS 259, at *86 (Del. Ch. Oct. 19, 1990); In re John Q. Hammons Hotels S’holder Litig., No. 758-CC, 2011 Del. Ch. LEXIS 1, at *15 (Del. Ch. Jan. 14, 2011); In re PetSmart, Inc., 2017 Del. Ch. LEXIS 89, at *86 (Del. Ch. May 26, 2017); In re Appraisal Solera Holdings, Inc., 2018 Del. Ch. LEXIS 256, at *75 (Del. Ch. July 30, 2018); In re Appraisal of Jarden Corp., 2019 Del. Ch. LEXIS 271, at *83, *89 (Del. Ch. July 19, 2019); In re Panera Bread Co., No. 2017-0593-MTZ, 2020 Del. Ch. LEXIS 42, at *97 (Del. Ch. Jan. 31, 2020). ↑

5. HBK Master Fund L.P. v. Pivotal Software, Inc., No. 2020-0165, 2024 Del. Ch. LEXIS 332, at *3 (Del. Ch. Mar. 12, 2024). ↑

6. See, e.g., Tim Koller, Marc Goedhart & David Wessels, Valuation: Measuring and Managing the Value of Companies 285–86 (John Wiley & Sons 7th ed. 2020). ↑

7. Christian Petersen, Thomas Plenborg & Finn Schøler, Issues in Valuation of Privately Held Firms, 10 J. Private Equity, at exhibit 7 (2006). ↑

8. Stephen Ross, Randolph Westerfield & Bradford Jordan, Fundamentals of Corporate Finance: A Handbook 165–67 (McGraw-Hill 12th ed. 2019); see also Joshua Rosenbaum & Joshua Pearl, Investment Banking: Valuation, Leveraged Buyouts, and Mergers & Acquisitions 132–33 (John Wiley & Sons 2009). ↑

9. To be clear, extrapolation per se is not the issue. The problem arises when extrapolating from projections that do not reflect a sustainable relationship between growth, profitability, and reinvestment. ↑

10. Ramcell, Inc. v. Alltel Corp., No. 2019-0601-PAF, 2022 WL 16549259, at *28 (Del. Ch. Oct. 31, 2022). ↑

11. Id. ↑

12. See Richard A. Brealey, Stewart C. Myers & Franklin Allen, Principles of Corporate Finance 292 (McGraw-Hill 13th ed. 2020) (“Profits that more than cover the cost of capital are known as economic rents. Economics 101 teaches us that in the long run, competition eliminates economic rents. That is, in a long-run competitive equilibrium, no company can expand and earn more than the cost of capital on that investment.”). ↑

13. Some valuation professionals allow for a spread above the cost of capital when determining the long-run return on investment. See Aswath Damodaran, The Dark Side of Valuation: Valuing Young, Distressed, and Complex Businesses 286 (Pearson 2d ed. 2010) (noting that, in some cases, a spread between the long-term return on investment and the cost of capital of up to 4 percent or 5 percent could be warranted). ↑

14. The general formula for terminal value under the convergence model is:

    

    where g is the terminal growth rate in after-tax operating profits (NOPAT), RONIC is the return on new invested capital, and WACC is the weighted average cost of capital. This valuation formula clearly allows for nonzero growth. However, when the return on new invested capital equals the weighted average cost of capital, the growth rate cancels out of the formula:

    

    The firm is growing at the rate g, but with a “lower” cash flow base that reflects an investment rate of g / WACC. The value of this growing firm is equivalent to another firm with no investment (and therefore a higher cash flow base) and no growth. ↑

15. HBK Master Fund L.P. v. Pivotal Software, Inc., No. 2020-0165, 2024 Del. Ch. LEXIS 332, at n.478 (Del. Ch. Mar. 12, 2024). ↑

Historically, employers have had discretion in deciding whether to offer group health plan coverage to their employees. For many businesses, this choice no longer exists, and compliant coverage must be offered pursuant to the Patient Protection and Affordable Care Act (the “ACA”) and Section 4980H of the Internal Revenue Code (the “Code”). Despite various challenges to the ACA, these mandates (and the corresponding penalties) are here to stay.

Under these rules, “applicable large employers” or “ALEs” are subject to significant penalties if they (1) fail to offer minimum essential coverage to at least 95 percent of their full-time employees (and their dependents) or (2) offer coverage that is not “affordable” or that does not provide “minimum value.” Employers must report offers of coverage to the Internal Revenue Service and to certain employees on an annual basis. Penalties for noncompliance can be significant.

An employer is considered an ALE if it employed at least fifty full-time and full-time equivalent employees in the preceding calendar year. This low threshold, combined with sophisticated counting rules and limited transition relief, means that small-but-growing businesses are often caught unaware and at risk of penalty.

This article is designed to help employers navigate the complexities of this space and assess their ongoing compliance efforts. Note, however, that the ACA and related guidance offer significantly more detail than what is contained here and should be consulted in all cases. Employers should not rely on this article as legal advice and should consult with legal counsel, as appropriate, based on their own facts and circumstances.

Part I: Determining Applicable Large Employer Status

A. The Rule

An employer must be considered an “applicable large employer” to be subject to a Section 4980H penalty with respect to a given calendar year. An “applicable large employer” is generally one that—in the preceding calendar year—employed at least fifty full-time employees or a combination of full-time and full-time equivalent employees that equals at least fifty. ALE status may change year-to-year and should be evaluated at least annually.

Example. In 2023, Company A employed forty full-time employees (i.e., employees averaging thirty or more hours per week) and ten full-time equivalent employees (e.g., twenty employees averaging fifteen hours per week). Company A is an applicable large employer for calendar year 2024.

B. The Calculation

An employer may follow the steps listed below to help determine its ALE status in a given year. Again, this calculation is for educational purposes only and should not be relied on as determinative on given facts and circumstances.

1. Identify the number of full-time employees (i.e., employees working 120 or more hours per month) in each month.
2. Add the total number of hours of service for all part-time employees (i.e., employees working less than 120 hours per month) in each month.
3. Divide the total hours of service for all part-time employees by 120 to determine the number of full-time equivalent employees for each month.
4. Add the number of full-time and full-time equivalent employees in each month. Do not round up or down.
5. Average across the year by adding the total number of full-time and full-time equivalent employees and dividing by 12. Round down to the next whole number.
6. If the average is 50 or greater, consider whether any special rules apply with respect to seasonal employees or otherwise.

C. Some Nuances

All types of employers can be ALEs. All employers are potentially subject to the employer shared responsibility rules, including for-profit, non-profit, and governmental entities.

Related companies are combined. Employers must count all full-time and full-time equivalent employees across all related employers. If the combined total of all such employees meets or exceeds the ALE threshold, then each related employer is considered an ALE (even if a given company has fewer than fifty full-time and full-time equivalent employees). For this purpose, related employers are determined by reference to the controlled group and affiliated service group rules set out in Code Sections 414(b), (c), (m), and (o).

Counting common-law employees. All common-law employees of the employer must be counted. As a result, careful consideration should be given to the correct classification of independent contractors and temporary employees. Failing to count all common-law employees could cause an employer to conclude incorrectly that it is not an ALE.

Certain individuals are excluded from the count. Sole proprietors, partners in a partnership, 2 percent S-corporation shareholders, leased employees (as defined in Code Section 414(n)(2)), and workers described in Code Section 3508 (i.e., certain real estate agents) are not considered “employees” for this purpose and are excluded from the count. In addition, most employees who work outside the United States are excluded, as are employees who have coverage under TRICARE or a Veterans Affairs’ health program.

Full-time employees. A full-time employee for any month is an employee (including a seasonal worker) who is employed for an average of at least thirty hours of service per week, or 120 hours of service in a calendar month.

Full-time equivalent employees. A full-time equivalent employee for any month is an employee (including a seasonal worker) who is not employed on a full-time basis. To determine the number of full-time equivalent employees in a month, (1) calculate the aggregate hours of service for all employees who are not full-time employees in the month, and (2) divide the total aggregate hours by 120. For this purpose, do not count more than 120 hours of service for any employee.

Hours of service. In general, an “hour of service” includes each hour for which an employee is paid or entitled to pay for the performance of duties and each hour for which the employee is paid or entitled to pay during a period for which no services are performed due to vacation, holiday, illness, incapacity (including disability), layoff, jury duty, military duty, or leave of absence. Certain unpaid leave must also be counted.

Hours for hourly employees. Hours of service include only actual hours with respect to hourly employees. Employers may not apply an equivalency or other estimate of hours.

Hours for salaried employees. Hours of service can consist of actual hours with respect to salaried employees. However, employers may substitute an equivalent number of hours for actual hours, including eight hours of service per day for any day during which the employee was paid or entitled to pay for at least one hour or forty hours of service per week for any week during which the employee was paid or entitled to pay for at least one hour. These equivalency methods must be applied uniformly across reasonable classifications of employees. Employers may not use an equivalency if it would result in a substantial underreporting of hours.

Seasonal worker exclusion. If the total number of full-time employees and full-time equivalent employees is fifty or greater for 120 or fewer days, or four or fewer calendar months, in a calendar year, and the employees in excess of fifty employed in that period were seasonal workers, the employer is not an ALE. The 120 days and the four calendar months are not required to be consecutive. For this purpose, employers may apply a reasonable, good faith interpretation of the term “seasonal worker.”

Part II: Calculating Penalties

A. The Rule

Large employers are subject to two potential coverage-related penalties: the Section 4980H(a) penalty and the Section 4980H(b) penalty. The former is often the larger and more significant of the two, as it is determined as a multiple of all full-time employees. ALEs may also be subject to significant reporting penalties. All penalties are determined monthly and are enforced by the Internal Revenue Service (IRS) on a rolling basis.

B. The Calculations

Section 4980H(a) penalty. A Section 4980H(a) penalty is due for any month in which (1) an employer does not offer “minimum essential coverage” to at least 95 percent of its full-time employees and their dependents and (2) at least one full-time employee receives a premium tax credit to help pay for coverage on a health insurance marketplace. This penalty applies with respect to all but thirty full-time employees, not just those who receive premium tax credits or those who were not offered coverage. The Section 4980H(a) penalty is paid by the employer. The annual Section 4980H(a) penalty for 2024 is $2,970 per employee to whom the penalty applies.

Section 4980H(b) penalty. A Section 4980H(b) penalty is due for any month in which (1) the coverage offered by the employer is unaffordable or does not provide minimum value and (2) at least one full-time employee receives a premium tax credit to help pay for coverage on a health insurance marketplace. Unlike the Section 4980H(a) penalty, the Section 4980H(b) penalty applies only with respect to employees who receive a premium tax credit. The Section 4980H(b) penalty is paid by the employer. The annual Section 4980H(b) penalty for 2023 is $4,460 per employee to whom the penalty applies.

Reporting penalties. A reporting penalty is due for any year in which the employer failed (1) to file complete and accurate information returns with the IRS or (2) to furnish complete and accurate information returns to applicable full-time employees. These penalties are determined by reference to the number of delinquent or inaccurate forms filed or furnished. The reporting penalties are paid by the employer. The penalty for 2024 is $310 (for failure to file) and $310 (for failure to furnish) per form.

C. Some Nuances

Penalties adjusted for inflation. Each of the penalties is adjusted annually for inflation.

Coverage penalties are alternatives. The IRS may assess only the greater of the Section 4980H(a) and Section 4980H(b) penalties in any given month. However, coverage penalties and reporting penalties may be assessed in tandem.

Related companies are not combined. Although the related business rules apply for determining whether an employer is an ALE, the penalty calculation applies on a company-by-company basis. Each company within a controlled group or affiliated service group separately files information returns and assesses penalty exposure.

Allocation of thirty-employee reduction. The Section 4980H(a) penalty permits a thirty-employee offset, which is allocated pro rata among an employer’s related companies. If a company’s allocation is fractional, round up to 1. The regulations do not permit employers to allocate this reduction at will (i.e., it must be spread ratably).

Offers of coverage. Employees must be provided an effective opportunity to accept or decline health insurance in order to be treated as having been offered coverage. An offer of coverage must be made to employees at least once each plan year.

Related company offers. An offer of coverage by one company to an employee for a calendar month is treated as an offer of coverage by all related companies for that calendar month.

The 95 percent test. The regulations provide that an ALE will be treated as offering coverage to substantially all its full-time employees and their dependents for a calendar month if it offers coverage to all but 5 percent, or if greater, five, of its full-time employees in that month.

Margin of error. Technically, employers have a 5 percent margin of error with respect to Section 4980H(a) compliance. Nevertheless, a penalty may still be assessed even for small, inadvertent errors (e.g., offers to 94.9 percent of full-time employees and their dependents).

Dependents. For this purpose, an employee is treated as having been offered coverage only if the employer also offers coverage to the employee’s dependents. A “dependent” means a natural child or an adopted child until the end of the month in which the child attains age twenty-six. Spouses, stepchildren, and foster children are not considered dependents and do not need to be offered coverage.

Minimum essential coverage. Minimum essential coverage is defined to include any group health plan or group health insurance that is not a Health Insurance Portability and Accountability Act (HIPAA) excepted benefit (e.g., standalone dental or vision plans).

Minimum value. A plan provides minimum value if it covers at least 60 percent of the total allowed cost of benefits that are expected to be incurred under the Plan. The Department of Health and Human Services developed a minimum value calculator to help determine if a plan with standard features provides minimum value. Plans with nonstandard features are required to obtain an actuarial certification. The regulations also describe certain safe harbor plan designs that satisfy the minimum value standard.

Affordability. Coverage is considered “affordable” if the lowest-cost self-only premium is less than 9.5 percent (as adjusted annually) of the employee’s annual household income. Given that household income is difficult to ascertain, the regulations include three safe harbors that may be relied upon to determine affordability. Use of the safe harbors is optional, and an employer may elect to apply them to any reasonable category of employees on a uniform and consistent basis. For this purpose, a reasonable classification can include specified job categories, nature of compensation, geographic location, and other bona fide business criteria.

Full-time employee count. The coverage penalties hinge on whether an ALE offers affordable, minimum essential coverage that provides minimum value to its full-time employees and their dependents. The IRS permits only two methods for determining whether an employee is “full time” for this purpose: the monthly measurement method and the look-back measurement method, which are beyond the scope of this article. These methods are different than the approach described in Part I with respect to determining ALE status.

Part III: Penalty Mitigation Strategies

Monitor ALE status. An employer’s status as an ALE may change from year-to-year, such that it is important (especially for small-but-growing businesses) to actively monitor employee count.

Section 4980H(a) penalty mitigation. To lower the risk of being assessed a penalty under Section 4980H(a), an employer should offer minimum essential coverage to at least 95 percent of its full-time employees and their dependents in each month of the year.

• Cost considerations. Certain employers elect to pay the penalty rather than bear the cost of insurance. Keep in mind that penalties are not deductible, unlike health insurance costs. Note also that employers may require employees to pay the full cost of coverage at the risk of incurring potentially smaller Section 4980H(b) penalties.
• Plan documents. Confirm that the group health plan documents provide for a thirty-hours-of-service requirement (or lower) and cover employees and dependents to age twenty-six. Some employers may decide to apply a more liberal eligibility definition (e.g., a twenty-hour standard) to provide a margin of error.
• Eligibility alignment. Consider using the look-back measurement method for both reporting and eligibility and adopting a corresponding policy.
• Margin of error. Monitor hours and employment status to preserve the 5 percent margin of error under the 95 percent test.

Section 4980H(b) penalty mitigation. To mitigate the risk of being assessed a Section 4980H(b) penalty, an employer should confirm that coverage provides minimum value and that the lowest-cost self-only premium satisfies the applicable affordability safe harbor for as many full-time employees as possible.

Reporting penalty mitigation. To mitigate the risk of being assessed reporting penalties, an employer should establish strong procedures for accurately and timely filing and furnishing Forms 1094-C and 1095-C. Many employers hire third-party vendors to complete reporting, but these relationships should be closely curated and monitored. Forms (and their underlying data) should be reviewed before filing, with special attention paid to the “offer of coverage indicator” and “full-time employee count” on Form 1094-C.

Matthew is grateful for the support and contributions of his partner, Nancy Campbell, in the preparation of this article.

Effective January 1, 2024, a new filing requirement was imposed on many business entities, particularly smaller privately held companies, by a new federal law named the Corporate Transparency Act (“CTA”). However, soon after, on March 1, 2024, a federal judge in the U.S. District Court for the Northern District of Alabama held the CTA to be unconstitutional as a matter of law.^[1] That case will undoubtedly be appealed. In National Small Business United v. Yellen, the district court concluded that the CTA exceeded the Constitution’s limits on congressional authority—specifically characterizing the CTA as regulating incorporation, which is a “purely internal affair” that is (i) not clearly economic or commercial in nature and (ii) too incidental to tax administration. Consequently, the court declared the CTA unconstitutional and permanently enjoined the United States Department of the Treasury and the Financial Crimes Enforcement Network (“FinCEN”), a bureau of the Treasury, from enforcing the CTA against the plaintiffs in that case, mostly members of the National Small Business Association (NSBA), the plaintiff organization that appeared before the court. Thus, the effect of this court decision is narrow and limited: the CTA remains in full effect except as to members of the NSBA and possibly reporting companies in the Northern District of Alabama. The following discussion concisely summarizes the CTA and what it entails to help businesses be prepared.

The CTA was enacted as part of the Anti-Money Laundering Act of 2020 and applies to entities deemed to be “Reporting Companies,” discussed below. The goal of the CTA is to address concerns about illicit activity by the use of obscure U.S. business entities—including money laundering, the financing of terrorism, tax fraud, human and drug trafficking, counterfeiting, piracy, securities fraud, financial fraud, and acts of foreign corruption—by requiring Reporting Companies to provide governmental authorities with information about their beneficial owners and controlling persons. The focus of the CTA is not on larger companies but rather on smaller and medium-sized legal entities, including shell companies, that generally either:

1. are not subject to supervision by other regulatory agencies (e.g., entities regulated by the Securities and Exchange Commission and Commodity Futures Trading Commission, or organizations that are tax-exempt under the Internal Revenue Code), or
2. employ fewer than twenty-one full-time employees and generate less than $5 million in annual U.S. revenue.

Specifically, a “Reporting Company” is: (a) any company that is created by filing a document with a governmental agency (including a federally recognized Indian Tribe), such as a corporation, a limited liability company, or a limited partnership; or (b) a foreign-formed entity that is registered or registers to do business in the United States. Although the CTA includes twenty-three categories of businesses that are exempt from the CTA, these exempt businesses generally are already extensively regulated by the federal government or a state government. Also exempt from the CTA, for example, are sole proprietorships and general partnerships, because they are not formed by filing a document with a governmental agency.

Reporting Companies are required to file a Beneficial Ownership Information Report, or “BOIR,” with FinCEN (more information can be found at FinCEN’s beneficial ownership information webpage). The BOIR requires information about the legal entity as well as personal information about those in substantial control of the Reporting Company. Such control can derive from (a) ownership interests (the BOIR must include anyone with 25 percent ownership interest of the entity), and/or (b) the right or ability to exercise substantial control over the legal entity through official positions or contractual, familial, or other arrangements. In addition, for a Reporting Company established or registered to do business in the U.S. in 2024 or later, the BOIR must also include information about its “Company Applicant(s),” which generally means (i) the person who directly files the pertinent document with a governmental agency and (ii), where more than one person is involved in the filing of such document, the person primarily responsible for directing or controlling that filing. If a person has reason to believe that a BOIR filed with FinCEN contains inaccurate information and voluntarily submits a report correcting the information within ninety days of the deadline for the original BOIR, then the CTA creates a safe harbor from penalty. In addition, once a Reporting Company has satisfied its CTA obligation by filing a BOIR, it must file an updated BOIR within thirty days of any change to the information about the entity or its beneficial owners, and it must file a corrected BOIR within thirty days of becoming aware of, or of when it should know of, any inaccuracy in the information about the entity or its beneficial owners on a filed BOIR. However, a Reporting Company is not required to file an updated or corrected BOIR if information about its Company Applicant changes. The information in a filed BOIR will not be available to the general public, but it will be available to federal and state law enforcement agencies.

Although the legal entity subject to the reporting requirement has the primary obligation to file a BOIR with FinCEN, individuals or other entities may also be found to have violated the CTA (e.g., because they caused the Reporting Company to fail to satisfy its reporting obligations). There are two ways a person can violate the CTA and be liable: (a) reporting violations (i.e., knowingly causing a Reporting Company not to timely file or update its BOIR or providing or assisting in the knowing provision of false or fraudulent information to FinCEN); or (b) disclosure-and-use violations (i.e., knowingly disclosing or using beneficial ownership information provided to FinCEN for an unauthorized purpose). For reporting violations, the CTA establishes: (i) civil penalties of up to $500 for each day a violation continues or has not been remedied; and (ii) criminal penalties of up to $10,000, imprisonment for up to two years, or both. For disclosure-and-use violations, the CTA establishes: (i) civil penalties of up to $500 for each day a violation continues or has not been remedied; and (ii) criminal penalties of up to $250,000, imprisonment for up to five years, or both. Based on the foregoing, FinCEN will determine the appropriate enforcement response for willful failure to report complete or updated beneficial ownership information to FinCEN (or failure to report at all) as required under the CTA.

1. Nat’l Small Bus. United v. Yellen, No. 5:22-cv-1448-LCB (N.D. Ala. Mar. 1, 2024). ↑

Machine learning and artificial intelligence (AI) are having a moment. Some models are busy extracting information—recognizing objects and faces in video, converting speech to text, summarizing news articles and social media posts, and more. Others are making decisions—on loan approvals, detecting cyberattacks, bail and sentencing recommendations, and many other issues. ChatGPT and other large language models are busy generating text, and their image-based counterparts are generating images. Although these models do different things, all of them ingest data, analyze the data for correlations and patterns, and use these patterns to make predictions. This article looks at some legal aspects of using this data.

Defining Machine Learning and AI

Machine learning and AI are not quite the same, but they are often used interchangeably. One version of the Wikipedia entry for AI defines it as “intelligence of machines or software, as opposed to the intelligence of other living beings.” Some AI systems use predefined sets of rules (mostly made by human experts) to make their decisions, while other AI systems use machine learning, in which a model is given data and told to figure out the rules for itself.

There are two basic types of machine learning. In supervised learning, the input data used for model training has labels. For instance, if you were training a model to recognize cats in images, you might give the model some images labeled as depicting cats, and some images labeled as depicting items other than cats. During training, the model uses the labeled images to learn how to distinguish a cat from a non-cat. In unsupervised learning, the training data does not have labels, and the model identifies characteristics that distinguish one type of input from another type of input. In either type of learning, training data is used to train a model, and test or validation data is used to confirm that the model does what it is supposed to do. Once trained and validated, the model can be operated using production data.

Contracting for AI Solutions

Joe Pennell, Technology Transactions Partner at Mayer Brown, notes: “The approach to contracting for AI depends on where your client sits in the AI ecosystem. A typical AI ecosystem contains a number of parties, including talent (e.g., data scientists), tool providers, data sources, AI developers (who may assemble the other parties to deliver an integrated AI system or solution), and the end user, buyer, or licensee of the AI system or solution. The contracts between these parties will each have their own types of issues that will be driven by the unique aspects of specific AI solutions. For example, those might include the training data, training instructions, input/production data, AI output, and AI evolutions to be created during training and production use of the AI.”

Intellectual Property Considerations

In addition to, or in the absence of clear contract provisions, intellectual property rights may also govern AI models and training data, as well as the models’ inputs and outputs. Patent, copyright, and trade secret issues can all be implicated.

Patents (at least in the United States) protect a new or improved and useful process, machine, article of manufacture, or composition of matter. However, abstract ideas (for example, math, certain methods of organizing human activity, mental processes), laws of nature, and natural phenomena unless integrated into a practical application are not patent-eligible. Case law delineating what is patent-eligible is a moving target. Thus, a model training or testing method, or a model itself, might be patentable, but not input data (because data is not a process or machine) or output data (because only humans can be inventors—so far).

Copyright (at least in the United States) protects original works of authorship including literary, dramatic, musical, and artistic works, such as poetry, novels, movies, songs, computer software, and architecture—but not facts, ideas, systems, or methods of operation (although copyright may protect the way in which these things are expressed). Thus, input data, depending on what it is and how it is arranged, might be copyrightable, including as alleged in a much-covered copyright lawsuit recently filed by the New York Times against OpenAI. Because only humans can be copyright holders (at least so far), protecting AI output via copyright requires that a human must have played a role in generating the AI output, and the output must be sufficiently transformed from copyrighted input data. How much of a role? How much of a transformation? Courts are only beginning to grapple with these questions. In addition, model training/testing methods and the model itself are probably not copyrightable, because they’re not original works of authorship.

Trade secrets are information that is not generally known to the public and that confers economic benefit on its holder because the information is not publicly known, and trade secret protection only applies if the holder makes reasonable efforts to maintain its secrecy. So, a model’s architecture, training data, and training method might be protectable as a trade secret, but having to explain model output can defeat the required secrecy.

Privacy Considerations

Moreover, AI training and input data can often implicate privacy issues. Much of that data comes from sources that would be considered as some form of personal data under various federal or state laws.

US enforcement agencies—including the Consumer Financial Protection Bureau, the Equal Employment Opportunity Commission, the Federal Trade Commission (FTC), and the Civil Rights Division of the Department of Justice—have made it clear that they will use privacy as a lever to regulate AI. The FTC has even gone so far as to effectively confiscate AI models trained on data that was obtained or maintained in violation of privacy laws seven times in the last four years. However, beyond federal agencies, because the US currently lacks any generally applicable/non-sectoral data privacy law, much of the action to protect consumers may fall to the states. More than a dozen states have passed general data privacy laws. Some of these state laws, including the Colorado Privacy Act, and as proposed for the California Consumer Privacy Act, contain detailed requirements on privacy notifications and obtaining consent on certain forms of what they call “automated decision-making.”

The first state civil complaint concerning data privacy has already been filed, and state attorneys general have begun bringing actions under state unauthorized and deceptive practices (UDAP) acts. At current count, forty-one state UDAP laws entail a private right of action. Class action attorneys have used those UDAP laws, along with state constitutional privacy claims, to bring massive actions against data brokers.

From a European perspective, perhaps the greatest risk to businesses comes from training data. If the training data is personal data (and the definition of that in the GDPR is significantly wider than the definitions generally found in US state laws), the GDPR applies, and if the data underlying the AI has been processed in a manner that is not GDPR compliant, this could create significant risks and liability to the businesses who are using those data.

Counsel for any organization that uses AI or machine learning should be clear about what information has been collected and the basis of such collection, and they should also ensure that any required permissions have been obtained. With the enactment of the European Union’s Artificial Intelligence Act this year, the penalties for getting it wrong may be significant—and would be in addition to the penalties that might already apply under the GDPR.

AI Bias Risks

In addition to privacy issues, bias in training data can negatively impact the safety and accuracy of deployed AI solutions. Common biases found in datasets are biased labeling, over- or underrepresentation of a demographic, and data that reflects a society’s existing or past prejudices. Biased labeling occurs when a programmer labels or classifies data in a way that incorporates her own biases. Data that reflects a society’s existing or past prejudices creates a similar outcome without manual labeling because the datasets come from a society with systemic exclusion, stereotyping, and marginalization of different groups of people. Over- or underrepresentation in data occurs when the use case of the AI solution is broader or more diverse than the data on which it is trained.

To avoid liability, businesses should confirm that the training dataset of AI they use mirrors the diversity of the intended use case. Sometimes, a particular bias in the dataset is not known until model deployment. In such cases, pre-deployment testing, specifically for bias, is crucial. Companies are well advised to implement data governance standards and bias checks at key points, including in connection with dataset collection/selection, algorithm training, pre-deployment testing, and post-deployment monitoring. Risks can be substantially mitigated if anti-bias data governance is made an integral part of creating, training, and monitoring AI and machine learning models.

***

This article is based on a CLE program titled “Big Data, Big Problems: The Legal Challenges of AI-Driven Data Analysis” that took place during the ABA Business Law Section’s 2023 Fall Meeting. To learn more about this topic, listen to a recording of the program, free for members.

Recent market trends in the Canadian private equity landscape indicate a growing appetite for sponsor-led liquidity solutions amid challenging market conditions. There has been a notable increase in secondary transactions and alternative exit strategies as sponsors seek to unlock value and provide liquidity to investors. While traditional exit routes such as initial public offerings (IPOs) have become less viable due to market volatility and regulatory uncertainties, sponsor-led solutions offer greater flexibility and efficiency in achieving liquidity objectives. Secondary market transactions—including fund restructurings, tender offers, and strip sales—have emerged as preferred alternatives, enabling sponsors to optimize portfolio performance and generate returns for investors.

These trends underscore the importance of agility and innovation in navigating the evolving private equity (PE) landscape, with sponsors leveraging strategic partnerships and sophisticated financial instruments to maximize value and mitigate risks.

This article explores several potential alternatives that PE sponsors may employ to meet increasing demands to address the liquidity needs of the fund, investors, and portfolio companies.

Fund Restructuring

A fundamental aspect of PE funds is their limited lifespan. However, liquidating PE assets at the expiry of the fund’s term, usually within a ten-year time frame, may not always be an optimal strategy, especially in a challenging macroeconomic environment. In such circumstances, one sponsor-led solution would be to create a continuation fund to acquire one or more portfolio companies from the existing fund. Under this structure, sponsors can retain control over managing the fund’s assets for an extended period until these assets achieve their maximum potential.

Continuation funds typically have a shorter term than the existing fund (e.g., two to six years). Furthermore, the investors of the existing fund generally will have the following options when the continuation fund is established:

1. selling their interest in the existing fund and receiving a pro rata share of the cash purchase price for the transfer of the assets to the continuation fund,
2. rolling over their interest into the continuation fund, or
3. occasionally, a combination of the previous options.

In the rollover option, investors may be allowed to roll over their interest on either a reset or a status quo basis. On a reset basis, the investor participates in the continuation fund on updated economic terms, which could involve lower management fees and higher carried interest rates. In return for the favorable economic terms under the reset basis, the sponsor would seek to lock in its carried interest earned in managing the existing fund to date. On a status quo basis, investors continue to participate in the continuation fund on substantially the same economic terms (i.e., same management fees and carried interest and no crystallization of carried interest on the transferred assets).

Numerous factors must be carefully considered when forming a continuation fund, including tax implications and structural complexities. However, a critical aspect is to address the sponsor’s conflict of interest, ensuring it complies with its fiduciary obligations to investors. To mitigate the conflict of interest risk, the sponsor can undertake measures such as seeking a fairness opinion from an independent valuation expert and providing adequate disclosure to all investors with respect to the terms of the restructuring process.

Historically, continuation funds have not been widely utilized in Canada. However, in recent years, there has been a growing trend towards their adoption, offering investors the flexibility to either withdraw from their investment in the portfolio company or remain invested by rolling into the continuation fund.

Tender Offers

Sponsors may also consider organizing a secondary sale process directly to facilitate liquidity for existing investors, allowing them to either maintain their interest in the existing fund or sell their interest to a secondary buyer.

Compared to a fund restructuring, a tender offer represents a simpler alternative, as it does not involve establishing a continuation fund, freeing the sponsor from the complications of investor negotiations and expenses associated with a continuation fund transaction. Additionally, a tender offer may prove particularly advantageous when the secondary buyer commits to subscribe for a “stapled” interest in another fund being raised by the sponsor.

Strip Sales

In a strip sale, the sponsor partially sells the fund’s portfolio company investments at a price negotiated with the secondary buyer. Buyers in these transactions typically consist of other PE funds that do not intend to acquire a controlling stake in the underlying assets. These sales offer partial liquidity in a well-performing portfolio without surrendering complete control of the underlying assets. However, it implies that the existing fund will surrender a percentage of the potential appreciation of the assets.

Similarly to when considering a continuation fund, sponsors considering strip sales should carefully review conflict of interest issues and financing arrangements of the portfolio companies.

Preferred Equity Options

Another sponsor-led liquidity strategy is preferred equity options, which allow a new investor to inject additional capital into the fund, in exchange for which the new investor receives priority over the distributions from the assets held by the fund.

This type of mechanism is typically structured by transferring the assets to a newly established special-purpose vehicle, which issues preferred shares to the new investor. Alternatively, the sponsor may admit the new investor to the fund and issue a preferred interest to such investor.

This strategy offers the benefit of providing liquidity to existing investors while contributing extra capital to the fund. Nonetheless, these transactions may require an amendment to the fund documentation to allow the issuance of preferred equity, typically requiring a higher level of consent from the limited partners.

Net Asset Value (NAV) Loans

Fund finance has traditionally consisted of subscription facilities, in which credit facilities are secured by the uncalled capital commitments of the limited partners. However, NAV loans have recently emerged as an attractive alternative to provide liquidity for funds when market conditions render asset sales difficult.

NAV loans, generally used for later-stage funds, allow sponsors to borrow against the value of their portfolio holdings, offering them flexible and efficient access to extra capital while avoiding potential discounts associated with other secondary market deals. NAV loans appeal to sponsors aiming to accelerate distributions to investors and finance add-on investors without requiring additional capital calls.

In NAV loan arrangements, lenders typically have recourse to the fund’s portfolio investments, with the borrowing base calculated on the net asset value of the fund’s portfolio assets. However, securing NAV loans will generally require a comprehensive due diligence review of the fund, the limited partners, and the portfolio assets. In this regard, reviewing the fund’s organizational documents is crucial to ascertain the feasibility of NAV finance. If the fund’s organizational documents do not expressly contemplate NAV borrowings, sponsors must carefully interpret the relevant borrowing provisions and determine the need for amendments or investor consent.

The current economic landscape continues to favor NAV loans, and sponsors should stay attuned to the evolving fund documentation and legal considerations surrounding these types of arrangements.

Risk Management and Mitigation Strategies

Effective risk management is paramount in sponsor-led liquidity transactions to safeguard investor interests and preserve value. Key risk factors include valuation uncertainties, conflicts of interest, regulatory compliance, and market volatility, all of which require proactive mitigation strategies.

Valuation Uncertainties:

Sponsors considering sponsor-led liquidity solutions must grapple with valuation uncertainties, particularly in volatile market conditions. The valuation of portfolio assets can fluctuate significantly, impacting the attractiveness and feasibility of liquidity options. To mitigate this risk, sponsors should employ robust valuation methodologies, leveraging industry best practices and engaging qualified valuation experts to ensure transparency and accuracy in the valuation process. Additionally, sponsors should conduct thorough due diligence on portfolio assets, scrutinizing financial performance, market dynamics, and potential risk factors to inform valuation assessments.

Conflicts of Interest:

Sponsor-led liquidity transactions inherently involve conflicts of interest, as sponsors seek to balance the interests of various stakeholders, including investors, portfolio companies, and themselves. To effectively manage conflicts of interest, sponsors should implement rigorous governance structures and adopt transparent communication practices throughout the transaction process. This may involve establishing independent committees or hiring third-party advisors to oversee the transaction and ensure fairness and impartiality. Moreover, sponsors should adhere to fiduciary duties and regulatory requirements, prioritizing the best interests of investors and maintaining integrity and ethical standards in decision-making.

Financial and Operational Risks:

Sponsor-led liquidity solutions entail inherent financial and operational risks, including potential disruptions to portfolio company operations, exposure to adverse market conditions, and unforeseen liabilities. Sponsors should conduct comprehensive risk assessments and scenario analyses to identify and mitigate potential risks, developing contingency plans and risk mitigation strategies to safeguard against adverse outcomes. This may involve stress-testing liquidity options under various market scenarios, assessing the impact of financial covenants and performance metrics on portfolio assets, and implementing robust risk monitoring and management frameworks to proactively address emerging risks.

Investor Relations and Transparency:

Maintaining strong investor relations and transparency is essential for fostering trust and confidence among stakeholders throughout the sponsor-led liquidity process. Sponsors should communicate openly and transparently with investors, providing timely updates and disclosures regarding transaction developments, risks, and potential outcomes. This includes facilitating meaningful dialogue and engagement with investors, addressing concerns and inquiries promptly, and soliciting feedback to inform decision-making. By prioritizing investor relations and transparency, sponsors can mitigate concerns regarding conflicts of interest and enhance investor confidence in the transaction process.

By addressing these key risk management and mitigation strategies, sponsors can navigate the complexities of sponsor-led liquidity solutions with greater confidence and resilience, effectively managing risks and maximizing value for all stakeholders involved.

Conclusion

Determining the optimal liquidity alternative for a PE fund will depend on various factors associated with the existing market conditions, interest rates, and the fund’s valuation. Sponsors are encouraged to evaluate the different liquidity options available carefully, considering the fund’s investment strategy and the provisions outlined in its organizational documents and portfolio-level agreements. Moreover, in structuring sponsor-led transactions, sponsors must navigate other critical considerations, including the previously described risk management and mitigation strategies, as well as skillful negotiation of the economic terms of the proposed transaction.

Looking ahead, we anticipate sustained growth in sponsor-led solutions in the United States and Canada, as the need to maximize liquidity in today’s market remains a top priority for sponsors and investors. Through strategic planning and execution, sponsors will be well positioned to achieve optimal results.

John J. McCann, former chair of the American Bar Association Business Law Section (1992–1993), passed away on March 13, 2024, but his work as a lawyer and leader of the Business Law Section (BLS) lives on. Most notably, McCann was instrumental in his leadership role in creating a new publication for the Business Law Section: Business Law Today (BLT). Debuting in 1992 as a print magazine, it evolved during the last thirty years into an electronic magazine and then a dedicated business law website, www.businesslawtoday.org.

“The work of John McCann ensured that our members would receive a steady stream of analytical articles on a wide range of business law practices,” said Lynette Hotchkiss, BLT’s current editor-in-chief. “In many ways, John was a true visionary, and he has left this amazing content resource for business lawyers, students, and academics.”

Over the course of more than forty years in business law, McCann’s legal expertise and knowledge was invaluable to both his clients and his business law colleagues. A graduate of Columbia Law School, John was admitted to the New York, New Jersey, and Florida bars and was partner in the New York law firm of Donovan, Leisure, Newton & Irvine; in-house counsel to the Prudential Insurance Company; and in-house counsel to Orion Specialty Insurance Company.

His contributions to BLS content allowed him to be recognized as a BLS leader and led to his appointment as an officer, and then as chair of the Section for the 1992–1993 bar year.

“I clearly remember that one evening at a BLS leadership meeting at the Ritz-Carlton in Florida,” said Maury Poscover, former chair of the Business Law Section (1997–1998). “I was in the chair’s suite with Lorrie, my spouse, talking with Herb and Ruthie Wander. John walked in, and he was so excited because he had just been told that he would be nominated as secretary of the Section. The memory stands out because Lorrie and I were equally excited because our son had just told us he was engaged.”

In September 1991, McCann announced to the membership the creation of a new periodical, Business Law Today, that would have its debut during his bar year. “This 64-page magazine will be a significant member benefit,” said McCann. “Business Law Today will enable us to publish many of the excellent submissions that we are unable to publish in The Business Lawyer. It will provide committees with a vehicle for regular content on all the practice areas of business law.”

And John’s Business Law Today has now morphed into a website that features articles, videos, podcasts, and other business law resources. Truly, a significant contribution to the ABA’s Business Law Section and the legal profession.

Imagine receiving a layoff notice because an AI evaluation tool predicted a higher risk of future underperformance due to your age. Or picture repeatedly having job applications rejected, only to find out the cause was an AI tool screening out candidates with a disability. These are just a few examples of real-world AI bias in the realm of hiring and employment, a growing issue that has already resulted in several notable lawsuits. How can companies effectively take advantage of AI in their employment practices while minimizing legal risks? This article discusses employment laws applicable to AI discrimination and provides practical strategies for companies to prevent potential government investigations, lawsuits, fines, class actions, or reputational damage.

A. AI Bias

A recent IBM article defines AI bias as “AI systems that produce biased results that reflect and perpetuate human biases within a society, including historical and current social inequality.”^[1] Two major technical factors contribute to AI bias:

1. Training Data: AI systems develop their decision-making based on training data; when those data overrepresent or underrepresent certain groups, it can cause biased results. A typical example is a facial recognition algorithm trained on data that overrepresents white people, which may result in racial bias against people of color in the form of less accurate facial recognition results. Moreover, mislabeled data, or data that reflect existing inequalities, can compound these issues. Consider an AI recruiting tool trained on a dataset where some applicant qualifications were incorrectly labeled. This could result in the tool rejecting qualified candidates who possess the necessary skills but whose résumés were not accurately understood by the tool.
2. Programming Errors: AI bias may also arise from coding mistakes, wherein a developer inadvertently or consciously overweighs certain factors in algorithmic decision-making due to their own biases. In one good example discussed in the IBM piece, “indicators like income or vocabulary might be used by the algorithm to unintentionally discriminate against people of a certain race or gender.”

B. AI Employment Discrimination

Companies have increasingly used AI tools to screen and analyze résumés and cover letters; scour online platforms and social media networks for potential candidates; and analyze job applicants’ speech and facial expressions in interviews.^[2] In addition, companies are using AI to onboard employees, write performance reviews, and monitor employee activities and performance.^[3] AI bias can occur in any of the above use cases, throughout every stage of the employment relationship—from hiring to firing and everything in between—and can result in discrimination lawsuits.

In one notable example, the Equal Employment Opportunity Commission ( “EEOC”) settled its first AI hiring discrimination lawsuit in August 2023.^[4] In Equal Employment Opportunity Commission v. iTutorGroup, Inc.,^[5] the EEOC sued three companies providing tutoring services under the “iTutorGroup” brand name (“iTutorGroup”) on the basis that iTutorGroup violated the Age Discrimination in Employment Act of 1967 (“ADEA”) because the AI hiring program it used “automatically reject[ed] female applicants age 55 or older and male applicants age 60 or older,” resulting in screening out over 200 applicants because of their age.^[6] Subsequently, iTutorGroup entered into a consent decree with the EEOC, under which iTutorGroup agreed to pay $365,000 to the group of automatically rejected applicants, adopt antidiscrimination policies, and conduct training to ensure compliance with equal employment opportunity laws.

The ongoing Mobley v. Workday, Inc.^[7] litigation, one of the first major class-action lawsuits in the United States alleging discrimination through algorithmic bias in applicant screening tools, presents another warning. The plaintiff, an African-American man over the age of forty with a disability, claims that Workday provides companies with algorithm-based applicant screening software that unlawfully discriminated against job applicants based on protected class characteristics of race, age, and disability and thus violated Title VII of the Civil Rights Act of 1964, the Civil Rights Act of 1866,^[8] the ADEA, and the ADA Amendments Act of 2008 (“ADAAA”). On January 19, 2024, the court granted Workday’s motion to dismiss the case, with leave for the plaintiff to amend the complaint.^[9] On February 21, 2024, the plaintiff filed an amended complaint outlining further details to support his claims.^[10]

With the foresight to prevent the kind of lawsuits discussed above, Amazon took proactive measures in 2018 by ceasing using an AI hiring algorithm after finding it discriminated against women applying for technical jobs; after being trained on a dataset of mostly men, the tool preferred applicants who used words that are more commonly used by men in their resumes, such as “executed” or “captured,” among other issues.^[11]

These cases, along with Amazon’s decision to scrap its biased AI hiring tool, highlight the growing concern about algorithmic bias in recruitment. Given this evolving landscape, employers must carefully examine all applicable federal, state, and local laws, as well as EEOC guidelines, to ensure fair and unbiased hiring practices.

C. Governing Law

1. Federal Law

There is currently no federal law specifically targeting the use of AI in the employment context. However, most employers’ use of AI tools in their employment practices would be subject to federal laws prohibiting employment discrimination based on race, color, ethnicity, sex (including gender, sexual orientation, and gender identity), age, national origin, religion, disability, pregnancy, military services, and genetic information.

Below is a list of primary federal laws a company must consider when evaluating AI-based employment evaluation tools. The most highly litigated one is Title VII, which applies to private employers that employ fifteen or more employees.

1. Title VII of the Civil Rights Act of 1964 (“Title VII”)^[12]: prohibits employment discrimination based on race, color, religion, sex (including gender, pregnancy, sexual orientation, and gender identity), or national origin.
2. Section 1981 of the Civil Rights Act of 1866^[13]: prohibits discrimination based on race, color, and ethnicity.
3. The Equal Pay Act^[14]: prohibits sex-based wage discrimination.
4. The Age Discrimination in Employment Act^[15]: prohibits discrimination based on age (forty and over).
5. The Immigration Reform and Control Act^[16]: prohibits discrimination based on citizenship and national origin.
6. Title I and Title V of the Americans with Disabilities Act (“ADA”)^[17] (including amendments by the Civil Rights Act of 1991 and the ADAAA): prohibits employment discrimination against qualified individuals based on disability and those regarded as having a disability.
7. The Pregnant Workers Fairness Act^[18]: prohibits discrimination against job applicants or employees because of their need for a pregnancy-related accommodation.
8. The Uniformed Services Employment and Reemployment Rights Act^[19]: prohibits discrimination against past and current members of the uniformed services, as well as applicants to the uniformed services.
9. The Genetic Information Nondiscrimination Act^[20]: prohibits discrimination in employment and health insurance based on genetic information.

2. State and Local Law

To address concerns over the use of AI in employment, states and local governments have become more proactive. Three notable examples of legislation that have been enacted, discussed below, demonstrate the growing trend among policymakers to regulate AI usage in employment practices, underscoring the increasing importance placed on ensuring fairness and accountability in AI-driven decision-making.

i. Illinois

In 2020, Illinois adopted the Artificial Intelligence Video Interview Act (820 ILCS 42/1), which imposes several requirements on employers if they conduct video interviews and use AI analysis of such videos in their evaluation process. These requirements include (i) notifying applicants of the AI’s role, (ii) providing applicants with an explanation of the AI process and types of characteristics used for evaluating applicants, (iii) obtaining the applicants’ consent for such AI use, (iv) only sharing videos with those equipped with the expertise or technology to evaluate the applicant’s fitness for a position; and (v) destroying videos within thirty days of a request by the applicant.

ii. Maryland

While not explicitly targeting AI, Maryland’s 2020 facial recognition technology law prohibits an employer from using certain facial recognition services—many of which use AI processes—during job interviews unless the applicant consents.

iii. New York City

New York City began enforcing its law on Automated Employment Decision Tools (“AEDT Law”) on July 5, 2023. Under this law, passed in 2021, employers and employment agencies are prohibited from using an automated employment decision tool (“AEDT”), which includes AI, to assess candidates for hiring or promotion in New York City unless an independent auditor completes a bias audit of the AEDT before its use and the candidates who are New York City residents receive notice that the employer or employment agency uses an AEDT. A bias audit must include “calculations of selection or scoring rates and the impact ratio across sex categories, race/ethnicity categories, and intersectional categories.”^[21] For each violation, offenders could face penalties ranging from $375–$1,500.

3. EEOC Guidance

The EEOC enforces federal laws prohibiting discrimination in hiring, firing, promotions, training, wages, benefits, and harassment. Employers with at least fifteen employees, labor unions, and employment agencies are subject to EEOC review. The EEOC has the authority to investigate discrimination charges against employers and, if necessary, file a lawsuit. Therefore, even though EEOC guidance is not legally binding, it proves valuable for companies seeking to avoid potential investigations or lawsuits when using AI tools.

i. EEOC 2022 Guidance on the ADA and AI

In May 2022, the EEOC issued technical guidance addressing how the ADA applies to the use of AI to assess job applicants and employees.^[22] The guidance outlines several common ways that utilizing AI tools can violate the ADA, including, for example, relying on an algorithmic decision-making tool that intentionally or unintentionally excludes an individual with a disability, failing to provide necessary “reasonable accommodation,” or violating the ADA’s restrictions on disability-related inquiries and medical examinations.

Employers can implement practices recommended by the EEOC to effectively handle the risk associated with utilizing AI tools, such as the following:

1. Disclose in advance the factors to be measured with the AI tool, such as knowledge, skill, ability, education, experience, quality, or trait, as well as how testing will be conducted and what will required.
2. Ask employees and job applicants if they require a reasonable accommodation using the tool. If the disability is not apparent, the employer may ask for medical documentation when requested for a reasonable accommodation.
3. Once the claimed disability is confirmed, provide a reasonable accommodation, including an alternative testing format.
4. “Examples of reasonable accommodations may include specialized equipment, alternative tests or testing formats, permission to work in a quiet setting, and exceptions to workplace policies.”^[23]

ii. EEOC 2023 Guidance on Title VII and AI

In May 2023, the EEOC issued new technical guidance on how to measure adverse impact when AI tools are used for employment selection, titled “Select Issues: Assessing Adverse Impact in Software, Algorithms, and Artificial Intelligence Used in Employment Selection Procedures Under Title VII of the Civil Rights Act of 1964.”^[24]

Under this guidance, if the selection rate of individuals of a particular race, color, religion, sex, or national origin, or a “particular combination of such characteristics” (e.g., a combination of race and sex), is less than 80 percent of the rate of the non-protected group, then the selection process could be found to have a disparate impact in violation of Title VII, unless the employer can show that such use is “job related and consistent with business necessity” under Title VII.

If the AI tool is found to have an adverse impact under Title VII, the employer can take measures to reduce the impact or select a different tool. Failure to adopt a less discriminatory algorithm that was considered during the design process may subject the employer to liability.

Under both EEOC guidance documents discussed here, an employer will be held liable for the actions or inactions of an outside vendor who designs or administers an algorithmic decision-making tool on its behalf and cannot rely on the vendor’s assessment of the tool’s disparate impact.

D. Legal Strategies

Considering the applicable laws and EEOC guidance, it would be prudent for a company to consider the following strategies to reduce risk of AI bias in employment decisions:

1. Prior to signing a contract with a vendor who designs or implements an AI-based employment tool, as part of the vendor due diligence process, a company’s legal team should work closely with its IT and HR teams to review and evaluate the vendor’s tools, including reviewing assessment reports and historical selection rates, based on the applicable laws and EEOC guidelines.

   In addition, any employers who are subject to New York City’s AEDT Law should have an independent auditor conduct a bias audit before utilizing the AI tool.

2. To incentivize a vendor to deliver a high-quality, legally compliant AI tool while mitigating risks, carefully negotiate and draft the indemnity, warranty, liability cap carveouts, and other risk allocation provisions of the contract with the vendor. These provisions should obligate the vendor to bear liability for any issues arising from the use of the AI tool in employment contexts caused by the vendor’s fault.

3. Prepare detailed internal documents clearly explaining the AI tool’s operation and selection criteria based on the review mentioned in item a to protect the company in case of government investigations or lawsuits.^[25]

4. The legal team should work closely with HR and the IT team to conduct bias audits on a regular basis.

5. If an audit reveals the tool has disparate impacts at any point, the company should consider working with the vendor to implement bias-mitigating techniques, such as modifying the AI algorithms, adding training data for underrepresented groups, or selecting a different tool, unless the legal counsel determines that the use of this tool is “job related and consistent with business necessity.”

6. Provide advance notice to candidates or employees who will be impacted by AI tools in accordance with applicable laws and EEOC guidance.

7. Educate HR and IT teams regarding AI discrimination.

8. Keep track of legal developments in this area, especially if your company has offices nationwide.

Faced with the looming threats of EEOC enforcement actions, class action lawsuits, and legislative uncertainty, employers may understandably feel apprehensive about charting a course that includes using AI in hiring or HR. However, consulting with attorneys to understand legal requirements and potential risks associated with AI employment bias—along with adopting proactive measures outlined in this article, staying informed about legal developments, and fostering collaboration across legal, HR, and IT teams—can help organizations effectively mitigate risks and confidently navigate the intricate landscape of AI employment bias.

1. IBM Data and AI Team, “Shedding light on AI bias with real world examples,” IBM, October 16, 2023. ↑

2. Keith MacKenzie, “How is AI used in human resources? 7 ways it helps HR,” Workable Technology, December 2023. ↑

3. Aaron Mok, “10 ways artificial intelligence is changing the workplace, from writing performance reviews to making the 4-day workweek possible,” Business Insider, July 27, 2023. ↑

4. Annelise Gilbert, “EEOC Settles First-of-Its-Kind AI Bias in Hiring Lawsuit (1),” Bloomberg Law, August 10, 2023. ↑

5. Equal Employment Opportunity Commission v. iTutorGroup, Inc., No. 1:22-cv-2565-PKC-PK (E.D.N.Y. filed May 5, 2022) (Aug. 9, 2023, joint notice of settlement and request for approval and execution of consent decree). ↑

6. “iTutorGroup to Pay $365,000 to Settle EEOC Discriminatory Hiring Suit,” U.S. Equal Employment Opportunity Commission, September 11, 2023. ↑

7. 3:23-cv-00770-RFL (N.D. Cal. filed Feb. 1, 2023). ↑

8. 42 U.S.C. § 1981. ↑

9. Joseph O’Keefe, Evandro Gigante, and Hannah Morris, “Judge Grants Workday, Inc.’s Motion to Dismiss in Groundbreaking AI Class Action Lawsuit Mobley v. Workday,” Law and the Workplace (blog), Proskauer, January 24, 2024. ↑

10. Daniel Wiessner, “Workday accused of facilitating widespread bias in novel AI lawsuit,” Reuters, February 21, 2024. ↑

11. Rachel Goodman, “Why Amazon’s Automated Hiring Tool Discriminated Against Women,” American Civil Liberties Union, October 12, 2018. ↑

12. 42 U.S.C. § 2000e. ↑

13. 42 U.S.C. § 1981. ↑

14. 29 U.S.C. § 206(d). ↑

15. 29 U.S.C. §§ 621–634. ↑

16. Pub. L. 99-603, 100 Stat. 3359 (1986)), as codified as amended in scattered sections of Title 8 of the United States Code. ↑

17. 42 U.S.C. §§ 12101–12113. ↑

18. 42 U.S.C. §§ 2000gg–2000gg-6. ↑

19. 38 U.S.C. § 4311. ↑

20. 42 U.S.C. § 2000ff. ↑

21. “Automated Employment Decision Tools: Frequently Asked Questions,” NYC Department of Consumer and Worker Protection, June 6, 2023. ↑

22. “The Americans with Disabilities Act and the Use of Software, Algorithms, and Artificial Intelligence to Assess Job Applicants and Employees,” U.S. Equal Employment Opportunity Commission, May 12, 2022. ↑

23. Id. ↑

24. “Select Issues: Assessing Adverse Impact in Software, Algorithms, and Artificial Intelligence Used in Employment Selection Procedures Under Title VII of the Civil Rights Act of 1964,” U.S. Equal Employment Opportunity Commission, May 18, 2023. ↑

25. See Lena Kempe, “AI Risk Mitigation and Legal Strategies Series No. 5: Explainable AI,” LK Law Firm, January 11, 2024. ↑