Using Loglinear Model for Discrimination Discovery and Prevention

Document Type

Article - Abstract Only

Publication Date



Data models, Computational modeling, Markov processes, Data mining, Analytical models, Mathematical model, Data preprocessing, Decision making, Graph theory, Perceived membership, Discrimination discovery, Discrimination prevention, Decision attribute, Nonprotected attributes, Decision making, Conditional independence graph, Fitted graphical loglinear model, Discrimination patterns, Markov properties, Loglinear modeling


Discrimination discovery and prevention has received intensive attention recently. Discrimination generally refers to an unjustified distinction of individuals based on their membership, or perceived membership, in a certain group, and often occurs when the group is treated less favorably than others. However, existing discrimination discovery and prevention approaches are often limited to examining the relationship between one decision attribute and one protected attribute and do not sufficiently incorporate the effects due to other non-protected attributes. In this paper we develop a single unifying framework that aims to capture and measure discriminations between multiple decision attributes and protected attributes in addition to a set of non-protected attributes. Our approach is based on loglinear modeling. The coefficient values of the fitted loglinear model provide quantitative evidence of discrimination in decision making. The conditional independence graph derived from the fitted graphical loglinear model can be effectively used to capture the existence of discrimination patterns based on Markov properties. We further develop an algorithm to remove discrimination. The idea is modifying those significant coefficients from the fitted loglinear model and using the modified model to generate new data. Our empirical evaluation results show effectiveness of our proposed approach.


Principal Investigator: Xintao Wu

Acknowledgements: This work was supported in part by U.S. National Science Foundation (1646654) and U.S. National Institute of Health (1R01GM103309).

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