AI and the law is a vital upcoming profitable opportunity for lawyers, law firms, and law students. The gauntlet had been thrown. You see, I was the invited keynote speaker at a major legal industry conference and my heralded topic was squarely in my wheelhouse, namely Artificial Intelligence (AI) and the law (typically coined as AI & Law). Rather than being entirely heralded, maybe the more apt phrasing is to say that the topic was met with a mixture of excitement by some and outright eyebrow-raising skepticism by others. The assembled collection of several hundred law firm partners and associates murmured and questioned subtly whether anything about AI and the law especially needed to be known by them. AI was generally perceived as a pie-in-the-sky topic. On top of that contention, AI when combined with the law was equally or even further at the outreaches of what daily hard-working nose-to-the-grind lawyers would seem to be thinking about. I'm pleased to say that my remarks were well-taken and the response was quite positive, including that this was the first time many of them had ever heard a no-nonsense compelling and comprehensive case made for why lawyers ought to know about AI and the law. The discussion got those top-notch legal-minded gears going and the attendees had plenty to ruminate on. Let's see if the same can be said for those of you that might be interested or at least intrigued by the AI & Law topic. First, a vital facet to know is that AI & Law consists of two intertwined conceptions. I want to emphatically make clear-cut that these are both bona fide and rapidly expanding ways in which AI and the law are being combined. Many attorneys are only familiar with one or the other of the two perspectives, or oftentimes not familiar with either of the two. Depending upon your lawyering preferences, it is perfectly fine to concentrate on one of the two and not particularly focus on the other. By and large, lawyers that seem less inclined toward having an interest in technology are bound to keep their eye on the law as applied to AI, wherein you don't necessarily need to get your hands into the tech per se. Those lawyers that seem to relish the high-tech infusion into the legal realm are more apt to gravitate toward the realm of AI as applied to the law. You are welcome to embrace both aspects and do so with your head held high. I'll first herein do some meaty unpacking on the law as applied to AI. When referring to the law as applied to AI, you should immediately be thinking about the emerging litany of new laws seeking to govern the advent of AI systems. Laws are springing up like wildfire. International laws are coming forth about AI & Law, federal laws too, state laws also, and local laws aplenty, see my ongoing coverage at the link here and the link here, just to name a few.

In the law, sometimes there is a need to craft a somewhat fictional aspect for purposes of allowing the wheels of justice to spin freely and not get unduly gummed up. That's where legal fiction can handily come to play. Per the definition of the Cornell Law School's Legal Information Institute (LII), a legal fiction is formally denoted as "an assumption and acceptance of something as fact by a court, although it might not be, so as to allow a rule to operate or be applied in a manner that differs from its original purpose while leaving the letter of the law unchanged." This is done ostensibly in the pursuit of justice, but for which can also be more modestly employed in the interests of convenience or for other jurisprudential benefits. I am reminding you about the nature of legal fiction to provide a bit of a potential surprise or some might say a mind-bending bombshell about a loosely proposed legal fiction regarding AI. Some experts suggest that we might need to concoct a legal fiction associated with ascribing a form of legal personhood to AI systems.

Dr. Lance B. Eliot is a renowned global expert on AI, Stanford Fellow at Stanford University, was a professor at USC, headed an AI Lab, top exec at a major VC.

Machine learning research typically starts with a fixed data set created early in the process. The focus of the experiments is finding a model and training procedure that result in the best possible performance in terms of some selected evaluation metric. This paper explores how changes in a data set influence the measured performance of a model. Using three publicly available data sets from the legal domain, we investigate how changes to their size, the train/test splits, and the human labelling accuracy impact the performance of a trained deep learning classifier. We assess the overall performance (weighted average) as well as the per-class performance. The observed effects are surprisingly pronounced, especially when the per-class performance is considered. We investigate how "semantic homogeneity" of a class, i.e., the proximity of sentences in a semantic embedding space, influences the difficulty of its classification. The presented results have far reaching implications for efforts related to data collection and curation in the field of AI & Law. The results also indicate that enhancements to a data set could be considered, alongside the advancement of the ML models, as an additional path for increasing classification performance on various tasks in AI & Law. Finally, we discuss the need for an established methodology to assess the potential effects of data set properties.

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Artificial Intelligence (AI) is gradually and inexorably entering into the legal profession. There is the use of Natural Language Processing (NLP), which we already experience in everyday ordinary interaction with Alexa and Siri and has been increasingly added into various LegalTech systems such as used for contract management, e-Discovery, and the like. Another avenue of AI consists of Machine Learning and Deep Learning. These computational pattern matching techniques are being used to predict court rulings and are also employed to ferret out prior relevant cases amongst a large-scale corpus of online court records. One of the most fascinating and likely law-disruptive AI technologies involves AI-based legal reasoning systems. The notion is that the AI simulates the legal argumentation precepts of human attorneys and essentially carries out a limited form of legal reasoning. Initially, these AI-based legal reasoners would be used as an aid for lawyers and jurists seeking to craft legal arguments. In this semi-autonomous mode, the AI works hand-in-hand with the human legal expert and they jointly establish a robust legal argument or legal posture. Some assert that this capability by the AI will inevitably be further advanced and we will have available fully autonomous AI-based legal reasoning systems that can act in lieu of needing any human legal guidance.

Some would contend that there is the law and then there is everything else. You've likely heard that well-worn line before. It is certainly eye-catching and memorable. What makes the catchphrase especially interesting is that somewhere in that morass is so-called soft law, sitting somewhat in a no man's zone. Yes, there is a nebulous grey area that is not quite a law and yet oftentimes provides a law-like shaping and tonal directive toward what we can do, including whether our actions are seemingly lawful or ostensibly could veer into becoming unlawful.