Causal Discrimination Discovery Through Propensity Score Analysis

By Bilal Qureshi, Faisal Kamiran, Asim Karim, Salvatore Ruggieri. 2016

19 citations recorded by the source corpus.

Subfields: Causal Inference & Discovery, AI Ethics, Fairness & Society

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Citations in the corpus

On formalizing fairness in prediction with machine learning (2017, neutral): “These measures model fairness through tools from causal interference and can be considered as a sub-class of causal measures (39; 31).”
Path-Specific Counterfactual Fairness (2018, methodology): “Among them, the causal framework (Pearl 2000; Dawid 2007; Pearl, Glymour, and Jewell 2016; Peters, Janzing, and Schölkopf 2017) offers an intuitive and powerful way of reasoning about fairness, by viewing unfairness as the presence of an unfair causal effect of the sensitive attribute on the decisio”
Avoiding Discrimination through Causal Reasoning (2017, methodology): “Moreover, causality has already been employed for the discovery of discrimination in existing data sets by [14, 17].”
Anti-discrimination learning: a causal modeling-based framework (2017, methodology): “In [28], the authors attempted to obtain an unbiased discrimination measurement by using the statistical tool—propensity score.”
The Disparate Effects of Strategic Manipulation (2018, background): “Much of the previous work in algorithmic fairness has examined a particular undesirable proxy eect in which a classier’s features may be linked to socially signicant and legally protected attributes like race and gender, interpreting correlations that have arisen due to centuries of accumulated disa”
Open the Black Box Data-Driven Explanation of Black Box Decision Systems (2018, methodology): “• Causal extensions of the previous correlation-based measures, e.g., by propensity score reweighing [36] or by probabilistic causation confidence score derived from Suppes-Bayes causal networks [4].”
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning (2018, background): “…anti-classiﬁcation requires that: Some authors have suggested stronger notions of anti-classiﬁcation that aim to guard against the use of unprotected traits that are proxies for protected attributes (Bonchi et al., 2017; Grgic-Hlaca et al., 2016; Johnson et al., 2016; Qureshi et al., 2016).”
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction (2018, background): “Our observations have important implications for recent proposals to avoid algorithmic discrimination through causal reasoning [8, 34, 39, 42, 51, 54, 63].”