Long-Term Fair Decision Making through Deep Generative Models
Yaowei Hu Yongkai Wu Lu Zhang
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.