The Impact of Algorithms on Judicial Discretion : Evidence from Regression Discontinuities

By Bo Cowgill. 2018

Subfields: AI Ethics, Fairness & Society

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Citations in the corpus

Measurement and Fairness (2019, background): “Indeed, interacting with measurements can create room for more individual judgments, automation bias, and (lack of) intuition and interpretability [6, 16, 28, 38, 55, 60].”
A Hierarchy of Limitations in Machine Learning (2020, background): “For example, work looking at how judges use recidivism prediction systems for decision support (Cowgill, 2018) shows that judges do not ignore the predictions (if they did, what would be the point?)”
Algorithmic realism: expanding the boundaries of algorithmic thought (2020, background): “Yet such assessments typically overlook the ways in which these “fair” algorithms can lead to unfair social impacts, whether through biased uses by practitioners [2, 29, 55, 56], entrenching unjust policies [54], distorting deliberative processes [51], or shifting control of governance toward unacco”
This Thing Called Fairness (2019, unknown): “of fairness and justice is complicated [33, 40, 115].”
The Principles and Limits of Algorithm-in-the-Loop Decision Making (2019, result): “There are several indications that our results accord with real-world outcomes, however: judges su er from many of same cognitive illusions as other people [38], are skeptical about the bene ts of algorithms [10, 11], and exhibit disparate interactions when using risk assessments [1, 16].”
The false promise of risk assessments: epistemic reform and the limits of fairness (2020, background): “being just above the thresholds for medium and high risk [32].”