The benefits and dangers of using machine learning to support making legal predictions

Abstract

Rule-based systems have been used in the legal domain since the 1970s. Save for rare exceptions, machine learning has only recently been used. But why this delay? We investigate the appropriate use of machine learning to support and make legal predictions. To do so, we need to examine the appropriate use of data in global legal domains-including in common law, civil law, and hybrid jurisdictions. The use of various forms of Artificial Intelligence, including rule-based reasoning, case-based reasoning and machine learning in law requires an understanding of jurisprudential theories. We will see that the use of machine learning is particularly appropriate for non-professionals: in particular self-represented litigants or those relying upon legal aid services. The primary use of machine learning to support decision-making in legal domains has been in criminal detection, financial domains, and sentencing. The use in these areas has led to concerns that the inappropriate use of Artificial Intelligence leads to biased decision making. This requires us to examine concerns about governance and ethics. Ethical concerns can be minimized by providing enhanced explanation, choosing appropriate data to be used, appropriately cleaning that data, and having human reviews of any decisions.