Mancuhan and Clifton: Combating discrimination using Bayesian networks

Koray Mancuhan and Professor Dr. Chris Clifton of Purdue University have published Combating discrimination using Bayesian networks , forthcoming in Artificial Intelligence and Law .

Here is the 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.

Filed under: Applications, Articles and papers, Research findings, Technology developments Tagged: Antidiscrimination law information systems, Artificial intelligence and law, Bayesian networks and legal information systems, Bayesian networks in antidiscrimination law information systems, Bayesian networks in discrimination detection systems, Bayesian networks in discrimination prevention systems, Bayesian statistical methods in legal informatics, Chris Clifton, Christopher W. Clifton, Civil rights law information systems, Discrimination detection systems, Discrimination prevention systems, Koray Mancuhan, Legal compliance information systems, Statistical methods in discrimination detection systems, Statistical methods in discrimination prevention systems, Statistical methods in legal informatics



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