mlsanityData to 7 Oct 2026

Navigating Fairness Measures and Trade-Offs

arXiv 2307.08484 ↗First posted 17 Jul 2023cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

In order to monitor and prevent bias in AI systems we can use a wide range of (statistical) fairness measures. However, it is mathematically impossible to optimize for all of these measures at the same time. In addition, optimizing a fairness measure often greatly reduces the accuracy of the system (Kozodoi et al, 2022). As a result, we need a substantive theory that informs us how to make these decisions and for what reasons. I show that by using Rawls' notion of justice as fairness, we can create a basis for navigating fairness measures and the accuracy trade-off. In particular, this leads to a principled choice focusing on both the most vulnerable groups and the type of fairness measure that has the biggest impact on that group. This also helps to close part of the gap between philosophical accounts of distributive justice and the fairness literature that has been observed (Kuppler et al, 2021) and to operationalise the value of fairness.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 17 Jul 2023.

Builds on

17 of 32 references.

References · 17

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  2. 2018Model Cards for Model Reportingcited by 1,068
  3. 2017Counterfactual Fairnesscited by 802
  4. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  5. 2017Algorithmic decision making and the cost of fairnesscited by 402
  6. 2018Delayed Impact of Fair Machine Learningcited by 183
  7. 2020Algorithmic Fairnesscited by 143
  8. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  9. 2020Survey on Causal-based Machine Learning Fairness Notionscited by 53
  10. 2021Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learningcited by 34
  11. 2022Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkitscited by 33
  12. 2020The Fairness-Accuracy Pareto Frontcited by 26
5 more
  1. 2022Selection, Ignorability and Challenges With Causal Fairnesscited by 16
  2. 2021Towards the Right Kind of Fairness in AIcited by 15
  3. 2022Counterfactual Fairness Is Basically Demographic Paritycited by 15
  4. 2021Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?cited by 10
  5. 2022To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontiercited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2024Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systemscited by 3
  2. 2025Gender Fairness of Machine Learning Algorithms for Pain Detectioncited by 3
  3. 2024Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimalitycited by 2
  4. 2025Voices of Freelance Professional Writers on AI: Limitations, Expectations, and Fearscited by 0
  5. 2026Equity Bias: An Ethical Framework for AI Designcited by 0