Fairness Constraints: A Flexible Approach for Fair Classification
By Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi. 2019
366 citations recorded by the source corpus.
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Citations in the corpus
- Characterizing Fairness Over the Set of Good Models Under Selective Labels (2021, background): “An influential literature on fair classification and fair regression constructs prediction functions that minimize loss subject to a predictive fairness constraint chosen by the decision maker (Dwork et al., 2012; Zemel et al., 2013; Hardt et al., 2016; Menon & Williamson, 2018; Donini et al., 2018;”
- Learning Fair Rule Lists (2019, methodology): “In this work, we compare the performance of FairCORELS to both C-LR (Zafar et al., 2019) and LAFTR (Madras et al.”
- Ethical Considerations in AI-Based Recruitment (2019, background): “For instance, when using natural language processing, a model can infer the gender of a candidate by name; this is known as indirect discrimination, or discrimination through use of features that are implicitly defined in the dataset [20].”
- Lagrangian Duality for Constrained Deep Learning (2020, methodology): “Finally, the Bank dataset [41], containing 41,188 samples, each with 20 attributes, where the task is to predict whether an individual has subscribed or not and the protected attribute is age.”
- Fairness in Machine Learning (2020, methodology): “Some methods transform the constrained optimization problem via the method of Lagrange multipliers [3, 15, 34, 37, 38, 59, 97, 135, 183, 185] or add penalties to the objective [5, 14, 22, 44, 46, 56, 58, 62, 73, 74, 75, 79, 82, 87, 88, 89, 93, 94, 95, 98, 104, 115, 119, 123, 133, 134, 135, 138, 139,”
- Robust Optimization for Fairness with Noisy Protected Groups (2020, background): “” As done by [14, 25, 55], we enforce equality of opportunity by equalizing true positive rates (TPRs).”