Artificial intelligence (‘AI’) is changing the world before our eyes. The prospects of AI to improve our lives is enormous. AI-based systems, moving forward, are likely to outperform humans in many fields including loosely-known noble professions, namely medical, legal to name just a few. Given that AI is being integrated in many conventional systems, so will apply in the administration of justice. Using AI tools would potential require legal professionals to adapt to these technologies. To that end, it is interesting to dissect how AI intersects with law, as well as how the former is relevant in the practice of law. A key motivation in writing this article is not being law-centric but to provide a realistic and intelligible view of AI development. Just briefly embarking on the description, AI is described as using technology to automate tasks that ‘normally require human intelligence’. The description of AI underlines that the technology is often focused upon automating specific types of tasks: those that naturally involve human intelligence to perform them. Perhaps an interesting question is: what forces allow AI to actually automate certain tasks, such as playing chess, translating languages, or driving car? Today, most successful artificial technologies fall into two broad categories: (1) machine learning and (2) logical rules and knowledge representation. For example, a machine learning algorithm automatically detects spam emails and sends them to spam folder rather than to mailbox. For AI logical rules and knowledge representation, the goal behind this area is to model real-world processes in a form that computers use, typically for the purposes of automation. As regards AI impact in the practice of law, this can be extrapolated based upon current AI achievements. AI technology tends to work best for activities where there are underlying patterns, rules, definitive right answers and semi-formal or formal structures that make up the process. By contrast, AI tends to work poorly, or not at all, in areas that are conceptual, abstract, value-laden, open-ended, judgement-oriented; require common sense or intuition; involve persuasion or arbitration conversation and so forth. Turning to how AI is being used in law. At its heart, AI involves the application of computer and mathematical techniques to make law more understandable, manageable, useful, accessible, or predictable. This conception traces way back in the 1600s, by Gotfried Leibniz, the mathematician who famously co-invented calculus, was also a trained lawyer, and was one of the earliest to investigate how mathematical formalisms might improve law. More recently, predictive-coding technologies have employed AI techniques, such as machine learning and knowledge representation, to help automate activity. Some of the machine learning e-discovery software can be trained to detect patterns of for e-mails and other documents likely to be relevant to the scope of the litigation. Another interesting use of machine learning in the practice of law is in the prediction of legal outcomes. Legal officers of the Court using machine learning systems can make predictions about the outcome of the cases and relying upon data, rather than instinct, to help their odds of winning a case. Besides, the machine learning software is used to predict the risk of reoffending that creates its predictive model based upon past police arrest records. This is useful as machine learning technology can detect, with higher probability, patterns from past crime data to attempt to predict the location and time of future crime attempts. There could be a number of ways to describe the machine learning life cycle largely based on the type of project, but in general there are five steps: data collection, data preparation, model development, model evaluation and post processing and model deployment. The writer is a law expert. The views expressed in this article are of the writer.
