Computational Fairness: Preventing Machine-Learned Discrimination

By Michael Feldman. 2015

37 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

Fair Resource Allocation in Federated Learning (2019, methodology): “Two common approaches are to preprocess the data to remove information about the protected attribute, or to post-process the model by adjusting the prediction threshold after classifiers are trained (Feldman, 2015; Hardt et al., 2016).”
Wasserstein Fair Classification (2019, methodology): “…that do not contain sensitive information during training (Beutel et al., 2017; Calders et al., 2009; Calmon et al., 2017; Edwards and Storkey, 2016; Feldman et al., 2015; Fish et al., 2015; Kamiran and Calders, 2009, 2012; Louizos et al., 2016; Zemel et al., 2013; ˇZliobaite et al., 2011).”
Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification (2018, background): “Finally, certain approaches attempt to edit posteriors in a way that satisfies fairness constraints [9, 10, 12, 20].”
Identifying and Correcting Label Bias in Machine Learning (2019, background): “One such approach has focused on developing post-processing steps to enforce fairness on a learned model (Doherty et al., 2012; Feldman, 2015; Hardt et al., 2016).”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “One consequence of this unfair resource allocation, a topic that is commonly explored in resource allocation for wireless networks [127] and ML [128].”
Fairness in Machine Learning (2020, methodology): “Pre-Processing methods consist in transforming the training data to remove undesired biases [16, 21, 24, 28, 47, 51, 52, 53, 61, 64, 65, 66, 67, 86, 90, 91, 92, 112, 114, 116, 118, 121, 122, 127, 126, 164, 174, 188, 190, 191, 193].”
Noise-tolerant fair classification (2019, methodology): “Recently, much progress has been made in this subject, both on devising sensible measures of fairness (Calders et al., 2009; Dwork et al., 2011; Feldman, 2015; Kim et al., 2018; Hardt et al., 2016; Zafar et al., 2017b,a; Kusner et al., 2017; Speicher et al., 2018; Zhang & Bareinboim, 2018; Heidari e”