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Poster
On Fairness and Calibration
Geoff Pleiss · Manish Raghavan · Felix Wu · Jon Kleinberg · Kilian Weinberger

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #74
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The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models. This has motivated a growing line of work on what it means for a classification procedure to be "fair." In this paper, we investigate the tension between minimizing error disparity across different population groups while maintaining calibrated probability estimates. We show that calibration is compatible only with a single error constraint (i.e. equal false-negatives rates across groups), and show that any algorithm that satisfies this relaxation is no better than randomizing a percentage of predictions for an existing classifier. These unsettling findings, which extend and generalize existing results, are empirically confirmed on several datasets.

Keywords: Fairness, Accountability, and Transparency
[ Code]  [ Paper] 

Author Information

Geoff Pleiss (Cornell University)
Manish Raghavan (Cornell University)
Felix Wu (Cornell University & ASAPP Research)
Jon Kleinberg (Cornell University)
Kilian Weinberger (Cornell University)

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