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Long-Term Fair Decision Making through Deep Generative Models (2401.11288v1)

Published 20 Jan 2024 in cs.LG and cs.CY

Abstract: This paper studies long-term fair machine learning which aims to mitigate group disparity over the long term in sequential decision-making systems. To define long-term fairness, we leverage the temporal causal graph and use the 1-Wasserstein distance between the interventional distributions of different demographic groups at a sufficiently large time step as the quantitative metric. Then, we propose a three-phase learning framework where the decision model is trained on high-fidelity data generated by a deep generative model. We formulate the optimization problem as a performative risk minimization and adopt the repeated gradient descent algorithm for learning. The empirical evaluation shows the efficacy of the proposed method using both synthetic and semi-synthetic datasets.

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Authors (3)
  1. Yaowei Hu (4 papers)
  2. Yongkai Wu (22 papers)
  3. Lu Zhang (373 papers)
Citations (1)
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