A comparative study of fairness-enhancing interventions in machine learning
Open accessAbstract
Computers are increasingly used to make decisions that have significant impact on people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers have appeared in the literature. This paper seeks to study the following questions: how do these different techniques fundamentally compare to one another, and what accounts for the differences? Specifically, we seek to bring attention to many under-appreciated aspects of such fairness-enhancing interventions that require investigation for these algorithms to receive broad adoption.
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There is a rich and rapidly evolving body of work in computer science for devising algorithmic remedies to bias and creating equitable ML models, which in turn offers a promising path for more equitable health care.
“Moreover, there is a rich and rapidly evolving body of work113–116 in computer science for devising algorithmic remedies to bias and creating equitable ML models, which in turn offers a promising path for more equitable health care.”
From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [116]
By not recognising the broader functions of automation in shaping power dynamics when considering bias mitigation, we run the risk of neutralising challenges in a way that actively facilitates discrimination and inequality under a banner of fairness.
“Of course, asking providers of AHSs to attend to the dynamics of power in labour relations and society more broadly might seem unnecessarily burdensome, but by not recognising the broader functions of automation in shaping those dynamics when considering forms of bias mitigation, we run the risk of neutralising challenges in a way that actively facilitates discrimination and inequality under a b”
From What does it mean to 'solve' the problem of discrimination in hiring?: social, technical and legal perspectives from the UK on automated hiring systems (2020) · cites this paper as [20]
Often approaches for fair ML are not stable, i.e., subtle changes in the training data significantly affect performance.
“[109,145] note that often approaches for fair ML are not stable, i.e., subtle changes in the training data signiicantly afect performance (comparatively high standard deviation).”
From Fairness in machine learning: A survey (2024) · cites this paper as [109]
[79,120,221] summarizes additional constraint types as precision or budget constraints, quantification or coverage constraints, churn constraints, and stability constraints.
“[79,120,221] summarizes a number of additional constraint types as precision or budget constraints to address the accuracy-fairness trade-of (often expressed as utility or risk functions e.g.[77,135]); quantiication or coverage constraints to capture disparities in class or population frequencies; churn constraints capturing online learning scenarios and enforcing that classiiers do not difer sign”
From Fairness in machine learning: A survey (2024) · cites this paper as [109]
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- Authors
- Friedler, S. A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E. P., & Roth, D.
- Affiliations
- Haverford College (US)
- University of Arizona (US)
- University of Utah (US)
- Venue
- Proceedings of the Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2019-01-29
- Pages
- 329-338
- OpenAlex
- W2786122898
- Cited by
- 601
- References
- 23
- Citations by year
- 2026: 39 · 2025: 65 · 2024: 97 · 2023: 75 · 2022: 90 · 2021: 100 · 2020: 77 · 2019: 49 · 2018: 8 · 2013: 1
- FWCI
- 82.94
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1513651, 1633724, 1633387)
- 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 › Imbalanced Data Classification Techniques
- Keywords
- algorithmic fairness, algorithmic decision-making, algorithmic discrimination, subpopulations
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