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Justicia: A Stochastic SAT Approach to Formally Verify Fairness.
Bishwamittra Ghosh, Debabrota Basu and Kuldeep S. Meel.
In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), February 2021.
As a technology ML is oblivious to societal good or bad,and thus, the field of fair machine learning has stepped up topropose multiple mathematical definitions, algorithms, andsystems to ensure different notions of fairness in ML applications.Given the multitude of propositions, it has becomeimperative to formally verify the fairness metrics satisfied bydifferent algorithms on different datasets. In this paper, wepropose a stochastic satisfiability (SSAT) framework, Justicia,that formally verifies different fairness measures of supervisedlearning algorithms with respect to the underlying data distribution.We instantiate Justicia on multiple classification andbias mitigation algorithms, and datasets to verify different fairnessmetrics, such as disparate impact, statistical parity, andequalized odds. Justicia is scalable, accurate, and operates onnon-Boolean and compound sensitive attributes unlike existingdistribution-based verifiers, such as FairSquare and VeriFair.Being distribution-based by design, Justicia is more robustthan the verifiers, such as AIF360, that operate on specific testsamples. We also theoretically bound the finite-sample errorof the verified fairness measure.
@inproceedings{GBM21,
title={Justicia: A Stochastic SAT Approach to Formally Verify Fairness},
author={ Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S.},
booktitle=AAAI,
month=feb,
year={2021},
bib2html_rescat={Formal Methods 4 ML},
bib2html_pubtype = {Refereed Conference},
bib2html_dl_pdf={../Papers/aaai21-gbm.pdf},
abstract={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 formally verifies different fairness measures of supervised
learning algorithms with respect to the underlying data distribution.
We instantiate Justicia on multiple classification and
bias mitigation algorithms, and datasets to verify different fairness
metrics, such as disparate impact, statistical parity, and
equalized odds. Justicia is scalable, accurate, and operates on
non-Boolean and compound sensitive attributes unlike existing
distribution-based verifiers, such as FairSquare and VeriFair.
Being distribution-based by design, Justicia is more robust
than the verifiers, such as AIF360, that operate on specific test
samples. We also theoretically bound the finite-sample error
of the verified fairness measure.},
}
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