Learning Fair Representations via an Adversarial Framework

30 Apr 2019Rui FengYang YangYuehan LyuChenhao TanYizhou SunChunping Wang

Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval. In this work, we consider the potential bias based on protected attributes (e.g., race and gender), and tackle this problem by learning latent representations of individuals that are statistically indistinguishable between protected groups while sufficiently preserving other information for classification... (read more)

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