The prediction of the potential outcome of a legal dispute is an important aspect in the provision of legal advice. Unfortunately legal data is far more open to bias and misinterpretation than it is in medicine and the natural and physical sciences. Explanation and governance are vital in legal domains and are thus examined in detail. This review is intended for a machine learning audience interested in legal and governance issues as well as the community who are interested in using data analytic tools to enhance legal decision-making.
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.
This article is categorized under: Commercial, Legal, and Ethical Issues > Legal Issues Commercial, Legal, and Ethical Issues > Fairness in Data Mining