Rawlsian Fairness for Machine Learning

By Matthew Joseph, Michael Kearns, Jamie H. Morgenstern, Seth Neel, Aaron Roth. 2016

88 citations recorded by the source corpus.

Subfields: Safety, Robustness & Interpretability

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In Safety, Robustness & Interpretability

Citations in the corpus

Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “Connections have been made between algorithmic fairness and Western concepts such as egalitarianism [38], consequentialism [142, 167], deontic justice [38, 142], and Rawls’ distributive justice [99, 142].”
Counterfactual Fairness (2017, background): “These include fairness through unawareness [12], individual fairness [10, 16, 24, 38], demographic parity/disparate impact [36], and equality of opportunity [14, 37].”
Fairness Without Demographics in Repeated Loss Minimization (2018, methodology): “In the online setting, works on fairness in bandit learning (Joseph et al., 2016; Jabbari et al., 2017) propose algorithms compatible with Rawls’ principle on equality of opportunity—an action is preferred over another only if the true quality of the action is better.”
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “These include: • Transparency (Bansal, Farhadi, and Parikh, 2014; Fallon and Blaha, 2018; Hayes and Shah, 2017; Wachter, Mittelstadt, and Russel, 2017), • Accountability (Adler, et al., 2018; Belotti and Edwards, 2001; Kroll et al, 2016), • Safety and privacy (Amodei, Olah, Steinhardt, Christiano, S”
Runaway Feedback Loops in Predictive Policing (2017, background): “In addition, it relates to the narrower question of defining notions of fairness in sequential learning settings such as the setting of bandits (regular, contextual and linear) and Markov decision processes[6, 5, 3, 4].”
Calibrated Fairness in Bandits (2017, background): “Joseph et al. [11] study a more general problem in which there is no group structure; rather, a number of individuals are available to select in each period, each with individual context (they also consider an infinite bandits setting.)”
Learning Non-Discriminatory Predictors (2017, background): “[7] Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth.”
Individual Fairness in Hindsight (2018, background): “Liu et al. (2017) and Gillen et al. (2018) study the model of Joseph et al. (2016b) under different notions of individual fairness.”
Fairness Incentives for Myopic Agents (2017, background): “[7] shows that these condence intervals are valid with probability 1 − δ .”
Non-Discriminatory Machine Learning through Convex Fairness Criteria (2018, background): “2017a), Rawlsian and meritocratic fairness (Joseph et al. 2016a),(Joseph et al.”