Gender, risk assessment, and sanctioning: The cost of treating women like men.

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Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment (2017, background): “These individuals contend that the most important consideration regarding bias is whether or not the probability estimates provided by the algorithm are well-calibrated, so that the algorithm performs in a way that is equally accurate across all groups [26, 64] This debate has led to a broader discu”
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2016, methodology): “These standards have recently been applied to the COMPAS [3] and PCRA [4] instruments, with initial findings suggesting that there is evidence of predictive bias when it comes to gender, but not when it comes to race [5, 6, 7].”
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning (2018, background): “Others have examined whether algorithmic predictions in criminal justice have similar AUC across protected groups, a form of classification parity (Skeem and Lowenkamp, 2016), and whether such predictions are calibrated (Skeem et al., 2016).”
Why Machine Learning May Lead to Unfairness: Evidence from Risk Assessment for Juvenile Justice in Catalonia (2019, background): “In this case, one could introduce an extra term that penalizes classifiers that yield different error rates [2, 24, 44].”