# Optimized Data Pre-Processing for Discrimination Prevention

arXiv: https://arxiv.org/abs/1704.03354 · HTML: https://mlsanity.com/paper/1704.03354/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2017.

## Built on by (most cited first, 12 of 12)

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- [Towards Fair and Explainable AI using a Human-Centered AI Approach](https://mlsanity.com/paper/2306.07427/) (2023)
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- [OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a Gradient Based Learning](https://mlsanity.com/paper/2404.10275/) (2024)
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## Abstract (arXiv)

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.