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Poster
Optimized Pre-Processing for Discrimination Prevention
Flavio Calmon · Dennis Wei · Bhanukiran Vinzamuri · Karthikeyan Natesan Ramamurthy · Kush Varshney

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #76
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Non-discrimination is a recognized objective in algorithmic decision making. In this paper, we introduce a novel probabilistic formulation of data pre-processing for reducing discrimination. We propose a convex optimization for learning a data transformation with three goals: controlling discrimination, limiting distortion in individual data samples, and preserving utility. We characterize the impact of limited sample size in accomplishing this objective. Two instances of the proposed optimization are applied to datasets, including one on real-world criminal recidivism. Results show that discrimination can be greatly reduced at a small cost in classification accuracy.

Keywords: Fairness, Accountability, and Transparency

Author Information

Flavio Calmon (Harvard University)
Dennis Wei (IBM Research)
Bhanukiran Vinzamuri (IBM Research)
Karthikeyan Natesan Ramamurthy (IBM Research)
Kush Varshney (IBM Research)

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