Legal artificial intelligence: Can it stand up in a court of law?
In his book Outliers, Malcolm Gladwell repeatedly mentions what has become known as the “10,000-hour rule”, which states that to become world-class in any field you must devote 10,000 hours of “deliberate practice”. Whether or not you believe the 10,000-hour figure, many would acknowledge that to become an accomplished legal professional requires considerable legal, communicative and, particularly in in-house environments, interpersonal skills that are often acquired after a tremendous amount of effort exerted over many years.
There has been much hoopla about AI-based legal systems that, some might have you believe, may soon replace lawyers (no doubt causing a degree of anxiety among some legal professionals). There is some misunderstanding among many lawyers, and much of the public, about what AI systems are presently capable of. Can a legal AI, based on current technology, actually “think” like a lawyer? No. At best, today’s AI is an incomplete substitute for a human lawyer, although it could reduce the need for some lawyers (I’ll get to all that later).
However, something we should think seriously about right now is the long-term implication of the introduction of AI into the legal environment—notably the potential loss of legal wisdom.
Why doesn’t AI think like a human?
Let’s explore why AI doesn’t actually mimic the human brain. As an example, let’s look at automated translation systems such as those available from Google, Facebook or Microsoft. Such systems might appear to work the way human translators do, but what they actually do is match patterns derived from analyses of thousands, if not millions, of pages of text found on the web, employing a technology known as statistical machine translation. For instance, if such a system wants to know how to translate the English greeting “hello” into French, it scans English and French translations on the web, statistically analyses the correlations between “hello” and various French greetings, then comes to the conclusion that the French equivalent of “hello” is “bonjour”.
Current AI is good at this kind of pattern matching, but less so at cognition and deductive reasoning. Consider the human brain: not only does it store a large number of associations, and accesses useful memories (sometimes quickly, sometimes not), it also transforms sensory and other information into generalisable representations invariant to unimportant changes, stores episodic memories and generalises learned examples into understanding. These are key cognitive capabilities yet to be matched by current AI technology.
Thus, while present AI-based legal systems might analyse judicial decisions—for example, to help litigators gain insights to a judge’s behaviour or a barrister’s track record—they do so by scrutinising existing data to reveal patterns, and not by extrapolating from the content of those decisions the way an experienced human legal professional might.
As AI systems become more capable, the temptation grows to use such systems not only to supplement but also to eliminate the need for some personnel. An AI system weak in cognition but strong in pattern matching probably could not replace an experienced professional in terms of drawing inferences, deductive reasoning or combining different practice areas to arrive at more comprehensive solutions.
