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Operationalizing Individual Fairness with Pairwise Fair Representations

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Lahoti,  Preethi
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1907.01439.pdf
(Preprint), 5MB

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Citation

Lahoti, P., Gummadi, K. P., & Weikum, G. (2019). Operationalizing Individual Fairness with Pairwise Fair Representations. Retrieved from http://arxiv.org/abs/1907.01439.


Cite as: http://hdl.handle.net/21.11116/0000-0003-FF17-5
Abstract
We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of a similarity metric. In this paper, we propose an operationalization of individual fairness that does not rely on a human specification of a distance metric. Instead, we propose novel approaches to elicit and leverage side-information on equally deserving individuals to counter subordination between social groups. We model this knowledge as a fairness graph, and learn a unified Pairwise Fair Representation(PFR) of the data that captures both data-driven similarity between individuals and the pairwise side-information in fairness graph. We elicit fairness judgments from a variety of sources, including humans judgments for two real-world datasets on recidivism prediction (COMPAS) and violent neighborhood prediction (Crime & Communities). Our experiments show that the PFR model for operationalizing individual fairness is practically viable.