PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
By Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong. 2019
130 citations recorded by the source corpus.
A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions. However, one common challenge of all causality-based fairness notions is identifiability, i.e., whether they can be uniquely measured from observational data, which is a critical barrier to applying these notions to real-world situations. In this paper, we develop a framework for measuring different causality-based fairness. We propose a unified definition that covers most of previous causality-based fairness notions, namely the path-specific counterfactual fairness (PC fairness). Based on that, we propose a general method in the form of a constrained optimization problem for bounding the path-specific counterfactual fairness under all unidentifiable situations. Experiments on synthetic and real-world datasets show the correctness and effectiveness of our method.
Subfields: Safety Research
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Safety Research
- Umang Bhatt
- Julius von Kügelgen
- Adrian Weller
- Bernhard Schölkopf
- Amir-Hossein Karimi
- Isabel Valera
- Silvia Chiappa
- Krishna P. Gummadi
Citations in the corpus
- On the Fairness of Causal Algorithmic Recourse (2020, background): “…a protected attribute on the prediction, thus making fairness a fundamentally causal problem (Kilbertus et al. 2017; Russell et al. 2017; Loftus et al. 2018; Zhang and Bareinboim 2018a,b; Nabi and Shpitser 2018; Nabi, Malinsky, and Shpitser 2019; Chiappa 2019; Salimi et al. 2019; Wu et al. 2019).”
- Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects (2020, neutral): “unfair for a setting at hand [21]–[25].”
- Causal Interpretability for Machine Learning - Problems, Methods and Evaluation (2020, background): “[102] unify all causal-based notions for fairness”
- Generative causal explanations of black-box classifiers (2020, background): “[45] Yongkai Wu, Lu Zhang, Xintao Wu, and Hanghang Tong.”