Fairness Without Demographic Data: A Survey of Approaches
Open accessAbstract
Detecting, measuring and mitigating various measures of unfairness are core aims of algorithmic fairness research. However, the most prominent approaches require access to individual level demographic information, such as sex or race. In practice, such information is often inaccurate, incomplete or entirely absent. A wealth of techniques have been proposed in response, such as proxies, trusted third parties and cryptographic solutions. These vary greatly in their assumptions, aims and costs, making it challenging to ascertain which methods might be appropriate for a given setting. We aim to clarify the landscape by providing an overview of the different proposals, surfacing the key dimensions in which they differ, and briefly discussing associated benefits, limitations and ethical considerations. We hope this will be of benefit to researchers, practitioners and regulators attempting to navigate this space.
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Algorithms can be biased even in the absence of demographic data, particularly through proxy discrimination, where a set of attributes acts as a proxy for the protected attribute.
“Algorithms can be biased even in the absence of demographic data, particularly through proxy discrimination, where a set of attributes acts as a proxy for the protected attribute (Ashurst and Weller, 2023).”
From Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool (2025) · cites this paper as Ashurst and Weller, 2023
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“These demographic groups are typically protected attributes under discrimination law, such as race, gender and age (Ashurst and Weller, 2023).”
- Authors
- Ashurst, C., & Weller, A.
- Affiliations
- The Alan Turing Institute (GB)
- University of Cambridge (GB)
- Venue
- Equity and Access in Algorithms, Mechanisms, and Optimization
- Publisher
- ACM
- Published
- 2023-10-30
- Pages
- 1-12
- OpenAlex
- W4388162395
- Cited by
- 21
- References
- 62
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 7
- FWCI
- 5.26
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) (publishedVersion, cc-by)
- Funders
- EPSRC (EP/V025279/1, EP/X03870X/1)
- Leverhulme Trust
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Discrimination and Equality Law
- Keywords
- algorithmic fairness, proxy variables, trusted third party, fairness measurement, fairness mitigation, ethical considerations, algorithmic discrimination
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