Credit: SimplySteno Court Reporting Blog
AI systems have an increasing ability to perform legal tasks that used to be within the exclusive province of lawyers. Anecdotally, it seems that both lawyers and the general public are getting more and more comfortable with the idea that legal grunt work–the drafting of contracts, reviewing voluminous documents, etc–can be performed by computers with varying levels of (human) lawyer oversight. But the idea of a machine acting as a judge is another matter entirely; people don’t seem keen on the idea of assigning to machines the task of making subjective legal decisions on matters such as liability, guilt, and punishment.
Consequently, I was intrigued when Thomas Dieterrich pointed me to the work of computer scientist Dr. Latanya Sweeney on “selective revelation.” Sweeney, a computer scientist by trade who serves as Professor of Government and Technology in residence at Harvard, came up with selective revelation as a method of what she terms “privacy-preserving surveillance,” i.e., balancing privacy protection with the need for surveillance entities to collect and share electronic data that might reveal potential security threats or criminal activity.
She proposes, in essence, creating a computer model that would mimic, albeit in a nuanced fashion, the balancing test that human judges undertake when determining whether to authorize a wiretap or issue a search warrant:
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The latest entry in my series of posts on autonomous weapon systems (AWSs) suggested that it would be exceedingly difficult to ensure that AWSs complied with the laws of war. A key reason for this difficulty is that the laws of war depend heavily on subjective determinations. One might easily expand this point and argue that AI systems cannot–or should not–make any decisions that require interpreting or applying law because such legal determinations are inherently subjective.
Ever the former judicial clerk, I can’t resist pausing for a moment to define my terms. “Subjective” can have subtly different meanings depending on the context. Here, I’m using the term to mean something that is a matter of opinion rather than a matter of fact. In law, I would say that identifying what words are used in the Second Amendment is an objective matter; discerning what those words mean is a subjective matter. All nine justices who decided DC v. Heller (and indeed, anyone with access to an accurate copy of the Bill of Rights) agreed that the Second Amendment reads: “A well regulated militia, being necessary to the security of a free state, the right of the people to keep and bear arms, shall not be infringed.” They disagreed quite sharply about what those words mean and how they relate to each other. (Legal experts even disagree on what the commas in the Second Amendment mean).
Given that definition of “subjective,” here are some observations.
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As AI systems become more widespread and versatile, they will undoubtedly have a major impact on our workforce and economy. On a macro scale–that is, across the labor market as a whole–whether AI’s impact will be positive or negative is very much an open debate. The same is true of the impact of AI on many specific occupations. Roughly half of jobs in the United States are “vulnerable” to automation, according to a 2013 study. But whether AI systems will prove “good” or “bad” for workers in a specific profession will depend in large part on whether AI serves as complement to human workers or acts as a replacement for them.
In the legal profession, for instance, the rise of predictive coding and improved scan-and-search software has given law firms the option of automating some of the most time-consuming (and therefore expensive) aspects of identifying relevant documents during litigation, a.k.a. document review. Document review has long been bread-and-butter work for young lawyers, especially at law firms that handle complex litigation cases, which can require sifting through and poring over thousands or even millions of pages of documents.
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