ARTIFICIAL INTELLIGENCE IN CRIMINAL JUSTICE ADMINISTRATION: THE USE OF MACHINE LEARNING FOR PREDICTIVE JUDICIAL ANALYSIS OF LEGAL DISPUTES

Abstract

The burgeoning advent of Artificial Intelligence (hereinafter referred to as AI) has taken the world by storm. This paper explores the interplay between machine learning and the current landscape of criminal justice administration in India. The discussion begins with an introduction to the core principles and central features of machine learning methodologies. The author shall then explain the concept of predictive justice, in context of machine learning methodologies, such as supervised learning and natural language processing. The Indian judicial system has been plagued by several challenges. Therefore, this paper seeks to examine the extent to which the use of predictive justice algorithms can aid and assist judges and magistrates, thereby enhancing judicial efficiency and transparency. Finally, the paper delves into the challenges posed by the use of predictive justice algorithms, with respect to the process of judicial decision making. Further, the paper critically evaluates concerns pertaining to transparency, accountability and preservation of constitutional principles, stemming from a potential risk of automation bias in the outcomes proposed by the predictive models. The author shall conclude the discussion by suggesting that a comprehensive regulatory framework is necessary to address the skepticism around AI driven outcomes, and to facilitate the integration of predictive justice analytics into the domain of dispute adjudication in a seamless manner.