Predict Responsibly: Increasing Fairness by Learning To Defer

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How researchers see this paper

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Learning Adversarially Fair and Transferable Representations (2018, methodology): “Therefore, if we only allow our adversary access to those outputs, our adversarial objective (assuming an optimal adversary), is equivalent to simply adding either ∆DP or ∆EO to our classification objective, similar to common regularization approaches (Kamishima et al., 2012; Bechavod & Ligett, 2017”
The Frontiers of Fairness in Machine Learning (2018, background): “There is also some work on fairness in machine learning in other settings — for example, ranking [YS17, CSV17], selection [KRW17, KR18], personalization [CV17], bandit learning [JKM + 18, LRD + 17], human-classiﬁer hybrid decision systems [MPZ17], and reinforcement learning [JJK + 17, DTB17].”
Algorithmic Fairness (2020, background): “This field is sometimes referred to as fair pipelines [21, 45, 52, 69, 97].”
A Review on Fairness in Machine Learning (2022, background): “This field is sometimes referred to as fair pipelines [29, 61, 71, 99, 142].”
Learning under selective labels in the presence of expert consistency (2018, unknown): “Our work shares similarities with the literature on learning to defer (Cortes et al., 2016; Madras et al., 2017), which also combines human and algorithmic decision making.”