Justicia: A Stochastic SAT Approach to Formally Verify Fairness
نویسندگان
چکیده
As a technology ML is oblivious to societal good or bad, and thus, the field of fair machine learning has stepped up propose multiple mathematical definitions, algorithms, systems ensure different notions fairness in applications. Given multitude propositions, it become imperative formally verify metrics satisfied by algorithms on datasets. In this paper, we stochastic satisfiability (SSAT) framework, Justicia, that verifies measures supervised with respect underlying data distribution. We instantiate Justicia classification bias mitigation datasets metrics, such as disparate impact, statistical parity, equalized odds. scalable, accurate, operates non-Boolean compound sensitive attributes unlike existing distribution-based verifiers, FairSquare VeriFair. Being design, more robust than AIF360, operate specific test samples. also theoretically bound finite-sample error verified measure.
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ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2021
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v35i9.16925