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arXiv · 1801.05398

On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning

Hao Wang·Berk Ustun·Flavio P. Calmon
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Abstract

In the context of machine learning, disparate impact refers to a form of systematic discrimination whereby the output distribution of a model depends on the value of a sensitive attribute (e.g., race or gender). In this paper, we propose an information-theoretic framework to analyze the disparate impact of a binary classification model. We view the model as a fixed channel, and quantify disparate impact as the divergence in output distributions over two groups. Our aim is to find a correction function that can perturb the input distributions of each group to align their output distributions. We present an optimization problem that can be solved to obtain a correction function that will make the output distributions statistically indistinguishable. We derive closed-form expressions to efficiently compute the correction function, and demonstrate the benefits of our framework on a recidivism prediction problem based on the ProPublica COMPAS dataset.

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BibTeXRIS

Hao Wang, Berk Ustun, Flavio P. Calmon. 2018-01-16. On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning. https://arxiv.org/abs/1801.05398

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