False Positives, False Negatives, and False Analyses: A Rejoinder to "Machine Bias: There's Software Used across the Country to Predict Future Criminals. and It's Biased against Blacks"

By Anthony W. Flores, Kristin Bechtel, Christopher T. Lowenkamp. 2016

1,388 citations recorded by the source corpus.

Subfields: Safety, Robustness & Interpretability

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Citations in the corpus

Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs (2021, methodology): “[3] investigated the actual risk scores assigned to specific defendants by Northpointe’s COMPAS recidivism-prediction system, while NIST’s disaggregated evaluation of commercially available face-recognition systems focused on potential performance dis-”
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (2016, background): “These and compensatory stereotypes are used by people to justify the status quo.”
Semantics derived automatically from language corpora contain human-like biases (2016, background): “Besides, bias is known to creep in indirectly, by proxy (Barocas and Selbst, 2014).”
Predictability and Surprise in Large Generative Models (2022, methodology): “ProPublica found that these risk scores are inaccurate and racially biased [2].”
Inherent Trade-Offs in the Fair Determination of Risk Scores (2016, background): “Moreover, this condition should hold when applied separately in each group as well [13].”
A Survey on Bias and Fairness in Machine Learning (2019, background): “However, like people, algorithms are vulnerable to biases that render their decisions “unfair” [6, 121].”
Generative Models for Effective ML on Private, Decentralized Datasets (2019, background): “When entire groups are unor underrepresented in training, the result can be biased predictions that induce quite insidious social effects (Angwin et al., 2016).”
Fair-by-design explainable models for prediction of recidivism (2019, background): “Such instruments may be used in pre-trial decision-making, prediction of locations where crimes most occur, proﬁles that are more likely to commit violent crimes, as well as to predict people who are likely to reoffend [1, 2].”
Please Stop Explaining Black Box Models for High Stakes Decisions (2018, background): “This is possibly the main flaw identified by criminologists [Flores et al., 2016] in the Propublica analysis [Angwin et al.”
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices (2018, background): “Second, when training fair learning models, practitioners face a tradeoff between accuracy and fairness [14, 23].”
Fairness Through Computationally-Bounded Awareness (2018, background): “Examples of discrimination in classification have been well-illustrated [24, 1, 5, 19, 13, 3]; nevertheless, developing a systematic approach to fairness has been challenging.”
Challenges for Transparency (2017, background): “Significant attention has focused on whether or not the prediction system is fair (Angwin et al., 2016).”
Price of Transparency in Strategic Machine Learning (2016, neutral): “, ”baked-in” discrimination and/or hidden motives [1].”