Man and machine: artificial intelligence and judicial decision making
Organizations: Leuven Centre for Empirical Jurisprudence · Leuven Centre for Empirical Jurisprudence Leuven Laboratory for Experimental Social Psychology
Abstract
The integration of artificial intelligence (AI) into judicial decision making -- particularly in pretrial, sentencing, and parole contexts -- has generated a substantial and rapidly growing literature. Across computer science, economics, law, criminology, and psychology, researchers have examined the reliability, fairness, and real-world effects of AI-assisted decision making. Yet this literature remains fragmented, and differences in assumptions, concepts, and research priorities make it difficult to assess what is actually known. Using criminal justice risk assessment as a focal case, this article makes two contributions. First, we develop a conceptual framework that distinguishes and relates three central questions: (1) the predictive validity of automated risk assessment tools; (2) how algorithmic risk assessments compare with human predictions (AI-versus-Human); and (3) how algorithmic recommendations affect judges' decisions (AI-plus-Human). Second, we use this framework to synthesize the empirical evidence addressing each of these questions. Our review identifies important limitations in existing research on predictive validity, as well as substantial gaps in understanding how judges respond to AI advice and how those responses vary across individuals and decision-making environments. The available evidence suggests that AI decision aids have, so far, had at most modest effects on pretrial and sentencing decisions. We conclude that further research is needed to understand how judges make decisions in noisy informational environments and under what conditions AI tools can produce meaningful improvements in judicial decision making.
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Supplementary material from the paper’s appendix.
Appendix
| Section | Studies |
|---|---|
| Predictive Validity | Desmarais et al.,2021 ; Duwe and Kim,2017 ; Fazel et al.,2012 ; Fazel et al.,2022 ; Ghasemi et al.,2021 ; Jung et al.,2017 ; Rudin et al.,2020 |
| Human-versus-Machine | Empirical research on human judges : Abrams et al., 2012; Ash et al., 2025; Beebe, 2006, 2007, 2020; Bonica & Sen, 2021; Bontrager et al., 2013; Braman & Nelson, 2007; Bushway et al., 2012; Bystranowski et al., 2021; Casper et al., 1989; Chen et al., 2016; Choi et al., 2022; Cohen & Yang, 2019; Dhami, 2003; Dhami & Ayton, 2001; Dyevre & Rodilla Lazaro, 2026; Eisenberg & Miller, 2004; Englich et al., 2006; Epstein et al., 2013; Fessel & Roese, 2011; Guthrie et al., 2000; Hangartner et al., 2025; Harley, 2007; Kukucka & Kassin, 2014; Levinson et al., 2017; Lu & Chen, 2025; O’Brien, 2009; Philippe, 2020; Pinello, 1999; Rachlinski et al., 2012, 2015; Rachlinski & Wistrich, 2021; Ramji-Nogales et al., 2007; Starr, 2015; Sunstein et al., 2007; Wistrich & Rachlinski, 2012. Human-versus-Machine comparisons : Ben-Michael et al.,2025 ; Biswas et al.,2020 ; Dressel and Farid,2018 ; Kleinberg et al.,2018 ; Lin et al.,2020 ; Rambachan,2024 ; Stevenson and Doleac,2024 |
| Human-plus-Machine | Angelova et al.,2025 ; Ben-Michael et al.,2025 ; Berk,2017 ; Green and Chen,2019 ; Grgić-Hlača et al.,2019 ; Imai et al.,2023 ; Lawson et al.,2024 ; Viljoen et al.,2019 |