Fair Boosting : a Case Study

By Benjamin Fish, Jeremy Kun, Á. Lelkes. 2015

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Citations in the corpus

Wasserstein Fair Classification (2019, background): “…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): “For example, it is common practice to assume that higher prediction errors of robust classifiers indicate mislabeled data [16], but we show (see Subsection 4.”
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].”
Identifying and Correcting Label Bias in Machine Learning (2019, background): “Examples include Calders et al. (2009); Kamiran and Calders (2009); ˇZliobaite et al. (2011); Kamiran and Calders (2012); Zemel et al. (2013); Fish et al. (2015); Feldman et al. (2015); Beutel et al. (2017).”
Nonconvex Optimization for Regression with Fairness Constraints (2018, background): “…of the existing papers in the latter category (Kamiran & Calders, 2010; Zliobaite et al., 2011; Kamishima et al., 2012a; Ristanoski et al., 2013; Fish et al., 2015; Hardt et al., 2016; Goh et al., 2016; Zafar et al., 2017a) deal with classification tasks, and thus cannot directly deal with…”