In her chapter titled “Artificial Intelligence and the Work of Visionary Boards” from the ABA’s new book Law of Artificial Intelligence and Smart Machines, Anastassia Lauterbach reports that her research has shown that “traditional boards are not sufficiently prepared to address and fully benefit from AI.” Here are some of her findings from the chapter.
Editor, Ted Claypoole
“Before starting with any AI implementation, directors have to understand three facts:
- It is becoming an increasingly obvious fact that certain mission critical business and compliance problems cannot and will not be properly solved without AI, including notably, cyber-security.
- The regulatory environment around AI is in flux. Technology once again is outpacing and out-flanking the legal and regulatory frameworks, creating confusion as well as opportunity.
- Companies must find talent that understands technology, but also has a keen ability to work cross-functionally. Business development and HR executives should rise to new prominence in companies that embrace emerging technologies such as AI.
. . .
There are several market-related factors boards need to understand in order to consider AI within their operational risk management frameworks and strategy oversight.
Technology leaders expect that within the next ten years AI as a stand-alone theme might disappear. It will get embedded into whatever product, service or process a company is designing and/or implementing. There are several market- and customer-related questions a board can ask to evaluate if a company should add Machine Learning components into its products and services. Some of these questions can also support decision making around AI vendors.
Cost to deploy. How much will it cost your customer not just to purchase your technology but also to change from their current solution to the new one? What is a minimum payback period in years in capital expenditures for a prospective customer, or for your company, if a vendor pitches a new Machine Learning solution?
Added-value beyond cost: What value does your Machine Learning-based software offer beyond labor substitution? Better quality, enhanced customer satisfaction, fewer errors, higher performance or throughput, something else?
Conflicting goals within potential customers: The scale at which AI or ML will eliminate or reduce human labor is likely to be significantly larger than any prior technology, resulting in potentially greater resistance. Will the human teams you are selling to lose their jobs as a result of your technology?
Regulatory/compliance issues: What are the current regulatory constraints that might complicate the adoption of your/the vendor’s offering? Besides technical challenges, humans tend to be more forgiving about mistakes made by humans as opposed to those made by AI, which might increase the liability hurdle on people overseeing automated systems.
Cybersecurity issues: The U.S. intelligence community reports that AI actually works in cybercriminals’ favor. Neural networks can be trained to create spams resembling a real email and become an agent for phishing attacks. Fake audio and video files can mimic voice. CAPTCHA bypassing seems to be very easy, exposing digital sign in. Most passwords can be breached with the brute force of Machine Learning. In 2017, the first publicly known example of AI for malware creation was proposed at Peking University in Beijing, when the authors created a MalGAN network.
Industry readiness: Sometimes an industry is just not ready to adopt a new solution because it is highly risk-averse. This occurs primarily in industries that are focused and incentivized on time-consuming activities rather than efficiency through new business models and technologies. An example of this can be seen in traditional utilities. Artificial Intelligence is sometimes incorrectly thought of as a “plug and play” or “black box” solution, when in fact it is not.
Vendors’ dynamics: Boards need to understand how top Internet brands, Machine Learning startups and traditional enterprise vendors compete. So far there are five full-stack AI companies, and all of them are among the global Fortune 10 list of the most valuable companies. These are Alphabet, Apple, Microsoft, Facebook and Amazon. I call these players “full-stack AI companies” as they control the whole technology stack—from semiconductors to devices—from their own platforms to ensure Machine Learning is utilized at every part of their organizations to build AI-powered consumer and business applications. Alphabet, Microsoft and Amazon are competing for dominance in cloud while constantly adding AI offerings. On the other hand, successful machine learning startups have deep domain expertise, and have concrete suggestions on how to solve their customers’ problems within a given legacy IT environment. Traditional enterprise vendors are jumping on the AI bandwagon, though they still have to demonstrate they can differentiate with their ML offerings.”
 James R. Clapper, “Statement for the Record. Worldwide Threat Assessment of the US Intelligence Community”, Senate Armed Services Committee, February 9th 2016.
 Suphannee Sivakorn, Jason Polakis, and Angelos D. Keromytis, “I’m not a Human: Breaking the Googe reCAPTCHA”, Columbia University, NY, 2016.
 “Weiwei Hu, Ying Tan, “Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN”, Peking University, Beijing, 2017, arxiv.org.
 Daniel Fagella, “AI Adoption – What it Takes for Industries to Change, HuffPost, August 1st 2017.