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This work tackles the issue of fairness in the context of generative
procedures, such as image super-resolution, which entail different
definitions from the standard classification setting. Moreover,
while traditional group fairness definitions are typically defined
with respect to specified protected groups -- camouflaging the
fact that these groupings are artificial and carry historical and
political motivations -- we emphasize that there are no ground
truth identities. For instance, should South and East Asians be
viewed as a single group or separate groups? Should we consider one
race as a whole or further split by gender? Choosing which groups
are valid and who belongs in them is an impossible dilemma and being
fair'' with respect to Asians may require being
unfair'' with
respect to South Asians. This motivates the introduction of
definitions that allow algorithms to be \emph{oblivious} to the
relevant groupings.
We define several intuitive notions of group fairness and study their incompatibilities and trade-offs. We show that the natural extension of demographic parity is strongly dependent on the grouping, and \emph{impossible} to achieve obliviously. On the other hand, the conceptually new definition we introduce, Conditional Proportional Representation, can be achieved obliviously through Posterior Sampling. Our experiments validate our theoretical results and achieve fair image reconstruction using state-of-the-art generative models.
Author Information
Ajil Jalal (University of Texas at Austin)
Sushrut Karmalkar (University of Texas at Austin)
Jessica Hoffmann (University of Texas at Austin)
Alexandros Dimakis (UT Austin)
Alex Dimakis is an Associate Professor at the Electrical and Computer Engineering department, University of Texas at Austin. He received his Ph.D. in electrical engineering and computer sciences from UC Berkeley. He received an ARO young investigator award in 2014, the NSF Career award in 2011, a Google faculty research award in 2012 and the Eli Jury dissertation award in 2008. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012. His research interests include information theory, coding theory and machine learning.
Eric Price (UT-Austin)
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2021 Spotlight: Fairness for Image Generation with Uncertain Sensitive Attributes »
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