Web: http://arxiv.org/abs/2205.02414

May 6, 2022, 1:11 a.m. | Avijit Ghosh, Matthew Jagielski, Christo Wilson

cs.LG updates on arXiv.org arxiv.org

In this work we explore the intersection fairness and robustness in the
context of ranking: \textit{when a ranking model has been calibrated to achieve
some definition of fairness, is it possible for an external adversary to make
the ranking model behave unfairly without having access to the model or
training data?} To investigate this question, we present a case study in which
we develop and then attack a state-of-the-art, fairness-aware image search
engine using images that have been maliciously modified …

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