… fairness and performance facing reinforcement learning … and evolution of the reinforcement
learning setting, we mod… and define fairness with respect to long-term rewards: a fair learning …

… In this section, we first recall this model and the reinforcement learning problem. We then …
for tackling fairness. We finish this section with an overview of fairness modeling and fair …

… fairness in machine learning has significantly progressed, our understanding of fairness in
reinforcement learning … Most of the attention has been on fairness in one-shot classification …

… fairness in the online Reinforcement Learning (RL) setting. We focus on the group notions
of fairness, … that learn the best fair policies without violating the fairness requirements at any …

… To truly build a fair decision support system, we envision the need to combine fairness
notions with explainable reinforcement learning, such that fairness can be taken into account …

… Here we introduce a reinforcement learning framework capable of mitigating biases that …
outcome fairness compared with current benchmarks and state-of-the-art machine learning …

… To resolve this problem, we propose a reinforcement learning based framework, FairRec, to
… accuracy and fairness in IRS. User preferences and the system’s fairness status are jointly …

… , we propose a fairness-aware recommendation framework using multi-objective reinforcement
learning (MORL), called MoFIR (pronounced “more fair”), which is able to learn a single …

… • We introduce a novel causal fairness notion called dynamics fairness. … in reinforcement
learning In this section, we aim to explore and explain the inequality in reinforcement learning …

… We propose a family of reinforcement learning algorithms that enable fairness utility
optimization by introducing multiplicative-adjusted rewards to obtain an actor-critic implementation. …