Long-Term Fair Decision Making through Deep Generative Models

• URL: http://arxiv.org/abs/2401.11288v1
• Date: Sat, 20 Jan 2024 17:44:50 GMT
• Title: Long-Term Fair Decision Making through Deep Generative Models
• Authors: Yaowei Hu, Yongkai Wu, Lu Zhang
• Abstract summary: This paper studies long-term fair machine learning which aims to mitigate group disparity over the long term in sequential decision-making systems. 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. We propose a three-phase learning framework where the decision model is trained on high-fidelity data generated by a deep generative model.
• Score: 12.333165351086171
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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