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Justicia: A Stochastic SAT Approach to Formally Verify Fairness
[article]
2021
arXiv
pre-print
As a technology ML is oblivious to societal good or bad, and thus, the field of fair machine learning has stepped up to propose multiple mathematical definitions, algorithms, and systems to ensure different notions of fairness in ML applications. Given the multitude of propositions, it has become imperative to formally verify the fairness metrics satisfied by different algorithms on different datasets. In this paper, we propose a stochastic satisfiability (SSAT) framework, Justicia, that
arXiv:2009.06516v2
fatcat:reyl3xmhyzbrhcia5eofllnxci