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Title: Achieving Long-Term Fairness in Sequential Decision Making
In this paper, we propose a framework for achieving long-term fair sequential decision making. By conducting both the hard and soft interventions, we propose to take path-specific effects on the time-lagged causal graph as a quantitative tool for measuring long-term fairness. The problem of fair sequential decision making is then formulated as a constrained optimization problem with the utility as the objective and the long-term and short-term fairness as constraints. We show that such an optimization problem can be converted to a performative risk optimization. Finally, repeated risk minimization (RRM) is used for model training, and the convergence of RRM is theoretically analyzed. The empirical evaluation shows the effectiveness of the proposed algorithm on synthetic and semi-synthetic temporal datasets.  more » « less
Award ID(s):
1910284
PAR ID:
10349352
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Volume:
36
Issue:
9
ISSN:
2159-5399
Page Range / eLocation ID:
9549 to 9557
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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