AI Regulation: What You Need To Know To Stay Ahead Of The Curve

• Published date: 25 June 2021
• Subject Matter: Technology, New Technology
• Law Firm: Arnold & Porter
• Author: Mr Peter J. Schildkraut

By Peter J. Schildkraut¹

Artificial intelligence (AI) is all around us. AI powers Alexa, Google Assistant, Siri, and other digital assistants. AI makes sense of our natural language searches to deliver (we hope) the optimal results. When we chat with a company representative on a website, we often are chatting with an AI system (at least at first). AI has defeated the (human) world champions of chess and Go.2²AI is advancing diagnostic medicine, driving cars and making all types of risk assessments. AI even enables the predictive coding that has made document review more efficient. Yet, if you're like one chief legal officer I know, AI remains on your list of things you need to learn about.

Now is the time! Right or wrong, there are growing calls for more government oversight of technology. As AI becomes more common, more powerful, and more influential in our societies and our economies, it is catching the attention of legislators and regulators. When a prominent tech CEO like Google's Sundar Pichai publicly proclaims "there is no question in my mind that artificial intelligence needs to be regulated," the questions are when and how-not whether-AI will be regulated.³

Indeed, certain aspects of AI already are regulated, and the pace of regulatory developments is accelerating. What do you need to know-and what steps can your company take-to stay ahead of this curve?

What Is AI?

Before plunging into the present and future of AI regulation, let's review what AI is and how the leading type works. There are many different definitions of AI, but experts broadly conceive of two versions, narrow (or weak) and general (or strong). All existing AI is narrow, meaning that it can perform one particular function. General AI (also termed "artificial general intelligence" (AGI)) can perform any task and adapt to any situation. AGI would be as flexible as human intelligence and, theoretically, could improve itself until it far surpasses human capabilities. For now, AGI remains in the realm of science fiction, and authorities disagree on whether AGI is even possible. While serious people and organizations do ponder how to regulate AGI⁴ -in case someone creates it-current regulatory initiatives focus on narrow AI.

Machine Learning

One type of AI, machine learning, has enabled the recent explosion of AI applications. "Machine learning systems learn from past data by identifying patterns and correlations within it."⁵ Whereas traditional software, and some other types of AI, run particular inputs through a preprogrammed model or a set of rules and reach a defined result (akin to 2+2=4), a machine learning system builds its own model (the patterns and correlations) from the data it is trained upon. The system then can apply the model to make predictions about new data. Algorithms are "now probabilistic. We are not asking computers to produce a defined result every time, but to produce an undefined result based on general rules. In other words, we are asking computers to make a guess."⁶

To take an example from legal practice, in a technologyassisted document review, lawyers will code a small sample of the document collection as responsive or not responsive. The machine learning system will identify patterns and correlations distinguishing the sample documents that were coded "responsive" from those coded "not responsive." It then can predict whether any new document is responsive and measure the model's confidence in its prediction. For validation, the lawyers will review the predictions for another sample of documents, and the system will refine its model with the lawyers' corrections. The process will iterate until the lawyers are satisfied with the model's accuracy. At that point, the lawyers can use the system to code the entire document collection for responsiveness with whatever human quality control they desire.

The quality of the training data set matters greatly. The machine learning system assumes the accuracy of what it is told about the training data. In the document review example, if, when the lawyers train the system, they incorrectly code every email written by a salesperson as responsive, they will bias the model towards predicting that every sales team email in the collection is responsive. Note that I did not say they will train the model to identify every...