Adding retrospective and prospective reward-disparity penalties to PPO lowers demographic parity and conditional statistical parity disparities in two multi-agent simulations, at a measurable efficiency cost.
Algorithms for Fairness in Sequential Decision Making
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abstract
It has recently been shown that if feedback effects of decisions are ignored, then imposing fairness constraints such as demographic parity or equality of opportunity can actually exacerbate unfairness. We propose to address this challenge by modeling feedback effects as Markov decision processes (MDPs). First, we propose analogs of fairness properties for the MDP setting. Second, we propose algorithms for learning fair decision-making policies for MDPs. Finally, we demonstrate the need to account for dynamical effects using simulations on a loan applicant MDP.
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Fairness Aware Reinforcement Learning via Proximal Policy Optimization
Adding retrospective and prospective reward-disparity penalties to PPO lowers demographic parity and conditional statistical parity disparities in two multi-agent simulations, at a measurable efficiency cost.