Prediction with Model-Based Neutrality

By Kazuto Fukuchi, Toshihiro Kamishima, Jun Sakuma. 2013

41 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety Research

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Citations in the corpus

Fair Regression: Quantitative Definitions and Reduction-based Algorithms (2019, background): “Kamishima et al. (2012) and Fukuchi et al. (2013) seek to ﬁt a probabilistic model that satisﬁes statistical independence, but they do not present efﬁcient algorithms or statistical guarantees.”
A survey on measuring indirect discrimination in machine learning (2015, methodology): “In discrimination literature this measure has been referred to as normalized prejudice index [Fukuchi et al. 2013], and used for measuring the magnitude of discrimination.”
Measuring discrimination in algorithmic decision making (2017, methodology): “In principle the same measure is expressed as where the requirement should be satisﬁed for all values of y and s , is referred to as η -neutrality (Fukuchi et al. 2013).”
Fairness in Machine Learning: A Survey (2020, background): “Reference [110] defines the concept of μ -neutrality that measures if probabilistic models are neutral with respect to specific variables and shows that this definition is equivalent to statistical parity.”
Fairness risk measures (2019, background): “Since the MI measures the degree of independence of two random variables, Λ MI is a natural measure of approximate demographic parity [Kamishima et al., 2012, Fukuchi et al., 2013, Calmon et al., 2017, Ghassami et al., 2018].”
Robust Text Classification in the Presence of Confounding Bias (2016, background): “Other approaches attempt to remove features that introduce bias (Pedreshi, Ruggieri, and Turini 2008; Fukuchi, Sakuma, and Kamishima 2013); however, existing approaches are not practical in text domains with tens of thousands of features.”
The cost of fairness in binary classification (2018, background): “This fairness-aware learning problem has received considerable attention in the machine learning community of late (Pedreshi et al., 2008; Kamiran and Calders, 2009; Calders and Verwer, 2010; Dwork et al., 2012; Kamishima et al., 2012; Fukuchi et al., 2013; Zafar et al., 2016; Hardt et al., 2016; Za”
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,”