From Soft Classifiers to Hard Decisions

Ran Canetti · Aloni Cohen · Nishanth Dikkala · Govind Ramnarayan · Sarah Scheffler · Adam Smith

36 citations0/0 files storedACM Conference on Fairness, Accountability, and Transparency (FAccT)

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Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI)Imbalanced Data Classification Techniques

Abstract

A popular methodology for building binary decision-making classifiers in the presence of imperfect information is to first construct a calibrated non-binary "scoring" classifier, and then to post-process this score to obtain a binary decision. We study various fairness (or, error-balance) properties of this methodology, when the non-binary scores are calibrated over all protected groups, and with a variety of post-processing algorithms. Specifically, we show:

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References

2016Equality of Opportunity in Supervised Learning 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 2017Algorithmic Decision Making and the Cost of Fairness 2017On Fairness and Calibration 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness 2018Fairness Under Composition 2011Fairness Through Awareness

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