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  Operationalizing Individual Fairness with Pairwise Fair Representations

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

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-FF17-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-FF19-3
Genre: Paper

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 Creators:
Lahoti, Preethi1, Author              
Gummadi, Krishna P.2, Author
Weikum, Gerhard1, Author              
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
2External Organizations, ou_persistent22              

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Free keywords: Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML
 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.

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Language(s): eng - English
 Dates: 2019-07-022019
 Publication Status: Published online
 Pages: 12 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1907.01439
URI: http://arxiv.org/abs/1907.01439
BibTex Citekey: Lahoti_arXiv1907.01439
 Degree: -

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