Preventing Disparate Treatment in Sequential Decision Making
Open accessAbstract
We study fairness in sequential decision making environments, where at each time step a learning algorithm receives data corresponding to a new individual (e.g. a new job application) and must make an irrevocable decision about him/her (e.g. whether to hire the applicant) based on observations made so far. In order to prevent cases of disparate treatment, our time-dependent notion of fairness requires algorithmic decisions to be consistent: if two individuals are similar in the feature space and arrive during the same time epoch, the algorithm must assign them to similar outcomes. We propose a general framework for post-processing predictions made by a black-box learning model, that guarantees the resulting sequence of outcomes is consistent. We show theoretically that imposing consistency will not significantly slow down learning. Our experiments on two real-world data sets illustrate and confirm this finding in practice.
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Relabelling is often used in counterfactual studies which investigate if flipping the (in)dependent variable(s) affect the classification outcome.
“Relabelling is also often used in counterfactual studies (e.g.[142,161,291]) which investigate if lipping the (in)dependent variable(s) afect the classiication outcome.”
From Fairness in machine learning: A survey (2024) · cites this paper as [142]
Some notable exceptions emphasise empirical risk subject to fairness constraints or welfare conditions.
“Some notable exceptions emphasise: 1) empirical risk subject to fairness constraints or welfare conditions (e.g.[87,142]), 2) TPR/FPR of protected groups (e.g.[19]), 3) stability of fairness (e.g.[145]), or 4) counterfactual terms (e.g.[85]).”
From Fairness in machine learning: A survey (2024) · cites this paper as [142]
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Cites
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- Authors
- Heidari, H., & Krause, A.
- Affiliations
- ETH Zurich (CH)
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 2248-2254
- OpenAlex
- W2808241018
- Cited by
- 35
- References
- 17
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 5 · 2022: 6 · 2021: 8 · 2020: 5 · 2019: 3 · 2018: 1
- FWCI
- 7.52
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- 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
- algorithmic fairness, disparate treatment, sequential decision making, consistency constraints, hiring decisions, job applications
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