The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Ghosting the Machine: Judicial Resistance to a Recidivism Risk Assessment Instrument
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“The second way that human bias might affect ARA tools is in the interpretation of generated risk scores [55].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [55]
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“Studies of risk assessment tools in use in the real world have found that judges frequently ignore the recommendations of those systems [55,66] and that judges sometimes interpret identical risk scores differently based on the race of defendants [57].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [55]
In the citing paper
“However, the fact that black defendants were sentenced more harshly during this time, even with the same risk score [55], shows that the tool itself may not have been at fault in this case.”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [55]
In the citing paper
“Likewise, judges express reluctance to adopt new risk assessments in their courtrooms [55], expressing concerns that risk assessments limit judicial options [55] and undermine elected officials.”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [55]
In the citing paper
“While AI-enabled ARA tools show potential for improving and supporting the criminal justice system, one common limiting factor is the unwillingness of human beings to engage with those tools [55].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [55]
In the citing paper
“Because of the disconnect between political and judicial priorities, risk assessment tools often take many years to develop [24], and their risk scores are often ignored by judges [55].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [55]
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Citations
Cites
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 4,567 · cited 1×
“Algorithmic systems are socially situated, interacting and entangling by necessity with people, institutional practices, and societal norms [4, 21, 34, 41].”
BackgroundAlgorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232 · cited 3×
“I argue, however, that this algorithm aversion cannot be accounted for by individuals’ distrust of the tools or automation anxieties, per the explanations given by existing scholarship [8, 16].”
BackgroundFair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159
Algorithms in practice: Comparing web journalism and criminal justice
Christin, A. (2017) · Big Data & Society · doi:10.1177/2053951717718855 · Cited by 379
A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores
De-Arteaga, M., Fogliato, R., & Chouldechova, A. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376638 · Cited by 221
Assessing Risk Assessment in Action
Stevenson, M. (2018) · Minnesota Law Review · doi:10.24926/265535.958 · Cited by 193
Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions
Alkhatib, A., & Bernstein, M. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300760 · Cited by 188
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- Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine Bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica (2016). https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
- David Mitre Becerril, Chris Bell, Katie LeFevre, Laurel Lin, Wilson Mui, Karan Shah, and Matt Hannigan. 2019. Validation and Assessment of Pennsylvania’s Risk Assessment Instrument, Pennsylvania Commission on Sentencing. Heinz College System Synthesis Project (May 2019).
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- CarlyWill Sloan George Naufal and Heather Caspers. 2018. The Effect of Risk Assessment Scores on Judicial Behavior and Defendant Outcomes. https://doi.org/10.2139/ssrn.3301699 · DOI
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Cited by
Sorted by citations
AI and the transformation of accountability and discretion in urban governance
Goldsmith, S., & Yang, J. (2026) · Urban Governance · doi:10.1016/j.ugj.2026.02.005 · Cited by 17
“The lack of interpretability can undermine trust, prompting resistance or selective disregard of AI-generated analysis or information ( Pruss, 2023 ).”
Disclosure or Marketing? Analyzing the Efficacy of Vendor Self-reports for Vetting Public-sector AI
Kuehnert, B., Johnson, N., Dotan, R., & Heidari, H. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3812220
“Performance evaluation is a central concern in public-sector AI deployment, where systems are often used in safety-or rights-critical contexts [1, 59, 62].”
Atkinson, G., & Casagrande, K. (2026) · Information · doi:10.3390/info17060574
Metadata
- Authors
- Pruss, D.
- Venue
- 2023 ACM Conference on Fairness Accountability and Transparency
- Publisher
- ACM
- Published
- 2023-06-12
- Pages
- 312-323
- Cited by
- 31
- References
- 7
- Funders
- Horowitz Foundation for Social Policy
- University of Pittsburgh Year of Data and Society
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