AI APPROACHES TO PREDICTIVE JUSTICE: A CRITICAL ASSESSMENT

Abstract

This paper addresses the domain of predictive justice, exploring the intersection of artificial intelligence (AI) and judicial decision-making. We will first introduce the concept of predictive justice, referring to the ongoing debate surrounding the potential automation of judicial decisions through AI systems. Then, we will examine the current landscape of AI approaches employed in predictive justice applications, providing a comprehensive overview of methodologies and technological advancements. Then, we delve into the phenomenology of predictive justice, highlighting the diverse spectrum of legal predictions achievable with contemporary AI systems. We also assess the extent to which these predictive AI systems are presently integrated into real-world judicial practices. Finally, the paper critically addresses recurrent fears and critiques associated with predictive justice. We sort these critiques into unreasonable objections, reasonable concerns with possible technical solutions, and reasonable concerns demanding further investigation. Navigating the complexities of these critiques, we offer some insights for future research and practical implementation. The nuanced approach taken in this study contributes to the ongoing discourse on predictive justice, emphasising the need for a balanced evaluation of its potential benefits and legal challenges.