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Using Artificial Intelligence In White-Collar Matters
Artificial intelligence (AI) is a key emerging technology that is poised to see vastly expanded use in many areas in which white-collar criminal practitioners work. AI currently is playing a growing role in helping white-collar lawyers and their clients analyze vast amounts of data to uncover insights, connections, and patterns that would be impossible to detect through manual reviews. As AI begins playing a more important role in compliance, fraud detection, and governmental investigations, regulators in the United States and around the world are adopting rules for how AI is implemented. Courts also are weighing in on the use of AI in litigation where it is used to analyze large amounts of electronically stored information (ESI). This article provides an introduction to AI technology and discusses the key regulatory developments practitioners should be aware of as they advise their clients on AI.
Background on AI
AI refers to computer processes that can mimic human cognitive skills, and computer scientists typically divide AI into Weak AI and Strong AI. Weak AI, also known as Narrow AI, is the type of AI used today to describe programs and algorithms that are designed to perform specific tasks such as playing chess, driving a car, and recognizing patterns in large data sets. Strong AI, also known as Artificial General Intelligence, is a theorized form of AI whereby a computer would have intelligence that is comparable to the human mind and includes generalized abilities to reason and analyze information.
White-collar practitioners should understand that there is a subfield within AI known as machine learning. Machine learning is a process by which computers use algorithms and training datasets to learn in a way that is similar to how humans learn, and it uses a small sample dataset to train a machine learning algorithm. Over time, a machine learning algorithm allows a computer to evaluate how accurately it is performing a specific task and takes corrective measures to eliminate errors in order to get better and better at the task. Once an algorithm is trained on the sample dataset, it can be deployed against much larger datasets to seek out patterns and other information that would otherwise remain hidden.
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