Fair Decisions Despite Imperfect Predictions

By Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera. 2019

70 citations recorded by the source corpus.

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Citations in the corpus

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020, background): “If successful recourse examples are included in future training data, this may help de-bias a system over time; we consider the intersection of our work with fair decision making in the context of a classifier evolving over time as the result of further data collection [25] a fruitful and important ”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “For example, it has been demonstrated that for models trained on selective labels , fair and optimal decisions should be made stochastically [25, 96].”
Adaptive Sampling Strategies to Construct Equitable Training Datasets (2022, background): “[40] consider the question of making predictions under partial and historically biased feedback.”
Fairness in Machine Learning: A Survey (2020, background): “References [163, 172, 186, 187, 298] note that there is an additional challenge in the datasets used to train models: Data represent past decisions, and as such, inherent bias(es) in these decisions are amplified.”
Optimal Decision Making Under Strategic Behavior (2019, methodology): “Finally, we deﬁne the (immediate) individual beneﬁt an individual with features x obtains as the probability that she receives a beneﬁcial decision, i.e. , where the function f ( ) is problem dependent.”
A Review on Fairness in Machine Learning (2022, background): “Commonly, causal methods for fairness mitigation are aimed at pre-processing the training data [44, 81, 83, 122, 128, 155, 174, 179].”