Meritocratic Fairness for Cross-Population Selection

By Michael Kearns, Aaron Roth, Zhiwei Steven Wu. 2017

56 citations recorded by the source corpus.

Subfields: AI Ethics, Fairness & Society

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Citations in the corpus

Beyond Individual and Group Fairness (2020, background): “There have been also studies of the problem of fair selection [Kearns et al., 2017, Kleinberg and Raghavan, 2018], online learning and multi-armed bandit problems [Liu et al., 2017, Joseph et al., 2018], in particular stochastic and contextual bandits [Joseph et al., 2016, Gillen et al., 2018,…”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Researchers have also developed a growing body of evaluation metrics for measuring fairness (Dwork et al., 2012; Kearns et al., 2017; Heidari et al., 2018; Verma and Rubin, 2018).”
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].”
On Fairness and Calibration (2017, background): “Fairness has also been considered in online learning [21, 24], unsupervised learning [5], and causal inference [25, 27].”
The Unfair Externalities of Exploration (2017, unknown): “Our work differs from the line of research on meritocratic fairness in online learning [9, 10, 14], which considers the allocation of limited resources such as bank loans and requires that nobody should be passed over in favor of a less qualified applicant.”
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees (2018, background): “There are increasingly many works on fairness in machine learning with provable guarantees, including [25, 34, 54, 57], that provide different classification algorithms with constraints on statistical parity or equalized odds, and [36, 40] for fairness in multi-armed bandit settings or ranking probl”
Equity of Attention: Amortizing Individual Fairness in Rankings (2018, background): “Various notions of group fairness have The notion of individual fairness [12, 24, 37] aims at treating concept of meritocratic fairness requires that less qualified candidates Prior work on fair rankings is scarce and includes approaches that Approach and Contribution. introduced here capture…”
Avoiding Resentment Via Monotonic Fairness (2019, background): “[28] explores the notion of monotonicity in the context of combining rankings between groups which lack common attributes, e.”