Fairness in Multi-Agent Sequential Decision-Making

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Beyond Individual and Group Fairness (2020, background): “…[Joseph et al., 2016, Gillen et al., 2018, Gupta and Kamble, 2019] and reinforcement learning [Jabbari et al., 2017, Doroudi et al., 2017, Wen et al., 2019, Zhang and Shah, 2014], human-classifier hybrid decision systems [Madras et al., 2018], or fair personalization [Celis and Vishnoi, 2017].”
Fairness without Demographics through Adversarially Reweighted Learning (2020, background): “At a high level they can be grouped into three categories, including (i) individual fairness [13, 56, 38, 37], (ii) group fairness [16, 32, 20, 55] that expects parity of statistical performance across groups, and (iii) fairness notions that aim to improve per-group performance, such as Pareto-fairn”