Bayesian Fairness
DOI:
https://doi.org/10.1609/aaai.v33i01.3301509Abstract
We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of Bayesian fairness as a suitable candidate for fair decision rules. Using balance, a definition of fairness introduced in (Kleinberg, Mullainathan, and Raghavan 2016), we show how a Bayesian perspective can lead to well-performing and fair decision rules even under high uncertainty.
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Published
2019-07-17
How to Cite
Dimitrakakis, C., Liu, Y., Parkes, D. C., & Radanovic, G. (2019). Bayesian Fairness. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 509-516. https://doi.org/10.1609/aaai.v33i01.3301509
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Section
AAAI Special Technical Track: AI for Social Impact