Fairness without Harm: Decoupled Classifiers with Preference Guarantees

By Berk Ustun, Yang Liu, D. Parkes. 2019

127 citations recorded by the source corpus.

Subfields: AI Ethics, Fairness & Society

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI Ethics, Fairness & Society

Citations in the corpus

A Survey on Bias and Fairness in Machine Learning (2019, background): “For instance, in [121] authors try to satisfy the subgroup fairness in the classification task, equality of opportunity and equalized odds in [55], both disparate treatment and disparate impact in [131], and equalized odds in [127].”
Statistical Equity: A Fairness Classification Objective (2020, background): “…also targets a wide variety of fairness deﬁnitions, such as equality of opportunity and equalized odds [Hardt et al., 2016, Woodworth et al., 2017], statistical parity [Agarwal et al., 2018], and subgroup fairness [Ustun, Liu, and Parkes, 2019], and their incorporation in classiﬁcation task.”
Learning Optimized Risk Scores (2016, neutral): “[111] Ustun, Berk, Yang Liu, and David Parkes.”
A Review on Fairness in Machine Learning (2022, background): “[199] propose using preference-based fairness measures that leverage notions from the fields of economics and game theory; Speicher et al.”
Ethical Machine Learning in Healthcare (2020, background): “While there is scholarship addressing social implications and algorithmic fairness in general, there has been less work at the intersection of health, ML, and fairness (14, 15, 16), despite the potential life-or-death impacts of those models (8, 17).”
Fairness Violations and Mitigation under Covariate Shift (2019, methodology): “Some alternative constraints have been defined, for example, using counterfactuals [55] or preference between group or aggregate-level models [71].”