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Combating discrimination using Bayesian networks

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Abstract

Discrimination in decision making is prohibited on many attributes (religion, gender, etc…), but often present in historical decisions. Use of such discriminatory historical decision making as training data can perpetuate discrimination, even if the protected attributes are not directly present in the data. This work focuses on discovering discrimination in instances and preventing discrimination in classification. First, we propose a discrimination discovery method based on modeling the probability distribution of a class using Bayesian networks. This measures the effect of a protected attribute (e.g., gender) in a subset of the dataset using the estimated probability distribution (via a Bayesian network). Second, we propose a classification method that corrects for the discovered discrimination without using protected attributes in the decision process. We evaluate the discrimination discovery and discrimination prevention approaches on two different datasets. The empirical results show that a substantial amount of discrimination identified in instances is prevented in future decisions.

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Acknowledgments

We wish to thank Alysa C. Rollock, J.D., Vice President for Ethics and Compliance at Purdue University, for discussion and pointers to relevant U.S. law. We also wish to thank journal reviewers for their helpful comments.

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Authors and Affiliations

  1. Purdue University, 305 N. University St., West Lafayette, IN, 47907, USA

    Koray Mancuhan & Chris Clifton

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  1. Koray Mancuhan
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  2. Chris Clifton
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Correspondence to Koray Mancuhan.

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Mancuhan, K., Clifton, C. Combating discrimination using Bayesian networks. Artif Intell Law 22, 211–238 (2014). https://doi.org/10.1007/s10506-014-9156-4

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