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Subjective Fairness

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Abstract

We introduce a natural, and widely applicable framework for fairness that relies on the available information. We develop algorithms for achieving a few different notions of fairness within a subjective framework, and in particular recently proposed concepts of fairness that are grounded in concepts of similarity and conditional independence. We argue that a suitable notion of similarity in the Bayesian setting is distributional similarity conditioned on the observations. For the latter, as independence is difficult to achieve uniformly in the Bayesian setting, we suggest a relaxation, for which we provide a small experimental demonstration.
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Dates and versions

hal-01531849 , version 1 (02-06-2017)
hal-01531849 , version 2 (10-07-2017)

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Christos Dimitrakakis, Yang Liu, David Parkes, Goran Radanovic. Subjective Fairness: Fairness is in the eye of the beholder. 2017. ⟨hal-01531849v2⟩
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