Man and machine: artificial intelligence and judicial decision making

• URL: http://arxiv.org/abs/2603.19042v2
• Date: Thu, 26 Mar 2026 10:56:37 GMT
• Title: Man and machine: artificial intelligence and judicial decision making
• Abstract summary: We conduct a synthetic review connecting three aspects of AI's role in judicial decision-making.<n>The impact of AI decision-aid tools on pretrial and sentencing decisions is modest or nonexistent.<n>We argue that AI-versus-human comparisons have the potential to yield new insights into both algorithmic tools and human decision-makers.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of artificial intelligence (AI) technologies into judicial decision-making, particularly in pretrial, sentencing, and parole contexts, has generated substantial concerns about transparency, reliability, and accountability. At the same time, these developments have brought the limitations of human judgment into sharper relief and underscored the importance of understanding how judges interact with AI-based decision aids. Using criminal justice risk assessment as a focal case, we conduct a synthetic review connecting three intertwined aspects of AI's role in judicial decision-making: the performance and fairness of AI tools, the strengths and biases of human judges, and the nature of AI-plus-human interactions. Across the fields of computer science, economics, law, criminology, and psychology, researchers have made significant progress in evaluating the predictive validity of automated risk assessment instruments, documenting biases in judicial decision-making, and, to a more limited extent, examining how judges use algorithmic recommendations. While the existing empirical evidence indicates that the impact of AI decision-aid tools on pretrial and sentencing decisions is modest or nonexistent, our review also reveals important gaps in the existing literature. Further research is needed to evaluate the performance of AI risk assessment instruments, understand how judges navigate uncertain decision-making environments, and examine how individual characteristics influence judges' responses to AI advice. We argue that AI-versus-human comparisons have the potential to yield new insights into both algorithmic tools and human decision-makers. We advocate greater interdisciplinary integration to foster cross-fertilization in future research.

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