Fair Inference on Outcomes
Open accessAbstract
In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are "sensitive," in the sense of having potential of creating discrimination. In this paper, we argue that the presence of discrimination can be formalized in a sensible way as the presence of an effect of a sensitive covariate on the outcome along certain causal pathways, a view which generalizes (Pearl 2009). A fair outcome model can then be learned by solving a constrained optimization problem. We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference.
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Causal methods are specifically well suited to identifying proxies of sensitive variables.
“Thus, causal methods are speciically well suited to identifying 1) proxies of sensitive variables as discussed in subsection 2.1; 2) which subgroups are most (un)fairly treated and diferentiate the types of bias exhibited [118]; and 3) provide transparency with respect to how (classiication) decisions were made [143].”
From Fairness in machine learning: A survey (2024) · cites this paper as [219]
[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 [219]
In-processing (constraint) optimization approaches have similar objectives to fairness regularization approaches.
“In-processing (constraint) optimization approaches (e.g.[3, 57, 68, 79, 120, 135, 174, 208, 218, 219, 221, 307, 313]) have similar objectives to fairness regularization approaches, and hence we present them together.”
From Fairness in machine learning: A survey (2024) · cites this paper as [219]
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- Authors
- Nabi, R., & Shpitser, I.
- Affiliations
- Johns Hopkins University (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-25
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- PMC
- PMC5963284
- OpenAlex
- W2617781997
- Cited by
- 20
- References
- 25
- Citations by year
- 2024: 3 · 2023: 2 · 2022: 6 · 2021: 1 · 2020: 3 · 2019: 2 · 2018: 3
- FWCI
- 4.55
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institute of Allergy and Infectious Diseases (R01AI104459, R01AI127271)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic fairness, discrimination, causal pathways, treatment effect estimation, observational data, constrained optimization, classification, regression analysis, semiparametric inference
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