Path-Specific Counterfactual Fairness
Open accessAbstract
We consider the problem of learning fair decision systems from data in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a counterfactual approach to disregard effects along unfair pathways that does not incur in the same loss of individual-specific information as previous approaches. Our method corrects observations adversely affected by the sensitive attribute, and uses these to form a decision. We leverage recent developments in deep learning and approximate inference to develop a VAE-type method that is widely applicable to complex nonlinear models.
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[65,173,219] transform the output of a classifier in accordance to the identification of unfair causal pathways, either by averaging, constraining the conditional distribution of the decision variable or through counterfactual correction.
“[65,173,219] transform the output of a classiier in accordance to the identiication of unfair causal pathways, either by averaging [219], constraining the conditional distribution of the decision variable [173] or through counterfactual correction [65].”
From Fairness in machine learning: A survey (2024) · cites this paper as [65]
Path-specific counterfactual fairness (PSCF) requires the knowledge of a full causal graph.
“In contrast to our proposed methods, path-specific counterfactual fairness (PSCF) requires the knowledge of a full causal graph.”
From Marrying Fairness and Explainability in Supervised Learning (2022) · cites this paper as [8]
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- Authors
- Chiappa, S.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- Corresponding author
- Chiappa, Silvia
- ORCID
- Chiappa, Silvia
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 7801-7808
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2788578887
- Cited by
- 16
- References
- 34
- Citations by year
- 2023: 2 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 3 · 2018: 3
- FWCI
- 2.87
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- counterfactual fairness, sensitive attributes, variational autoencoder, approximate inference, algorithmic fairness
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