Optimized Data Pre-Processing for Discrimination Prevention
This addresses discrimination prevention in algorithmic systems, which is a critical issue for fairness in society, though it appears incremental as it builds on existing pre-processing methods.
The paper tackled the problem of reducing discrimination in algorithmic decision-making by introducing a novel probabilistic formulation for data pre-processing, achieving simultaneous control of discrimination, distortion, and utility with results demonstrated on real-world datasets like criminal recidivism.
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, and apply two instances of the proposed optimization to datasets, including one on real-world criminal recidivism. The results demonstrate that all three criteria can be simultaneously achieved and also reveal interesting patterns of bias in American society.