![[2411.06624] A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2411.06624] A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning
Summary
This article reviews fairness in machine learning, emphasizing the need for context-appropriate fairness metrics and providing a flowchart to guide their selection.
Why It Matters
As AI regulations become stricter, understanding and applying fairness metrics is crucial for developers and policymakers. This guide addresses the complexities of fairness in machine learning, helping stakeholders navigate regulatory requirements and ethical considerations.
Key Takeaways
- Fairness in machine learning is context-dependent and cannot be measured by a single metric.
- A flowchart has been developed to assist in selecting appropriate fairness measures based on specific criteria.
- The article links fairness concepts to regulatory requirements, aiding compliance for AI developers and policymakers.
- Twelve criteria are outlined for evaluating fairness metrics, considering model assessment and data bias.
- Understanding fairness metrics is essential for addressing ethical concerns in AI applications.
Computer Science > Artificial Intelligence arXiv:2411.06624 (cs) [Submitted on 10 Nov 2024 (v1), last revised 17 Feb 2026 (this version, v4)] Title:A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning Authors:Caleb J.S. Barr, Olivia Erdelyi, Paul D. Docherty, Randolph C. Grace View a PDF of the paper titled A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning, by Caleb J.S. Barr and 3 other authors View PDF Abstract:Recent regulatory proposals for artificial intelligence emphasize fairness requirements for machine learning models. However, precisely defining the appropriate measure of fairness is challenging due to philosophical, cultural and political contexts. Biases can infiltrate machine learning models in complex ways depending on the model's context, rendering a single common metric of fairness insufficient. This ambiguity highlights the need for criteria to guide the selection of context-aware measures, an issue of increasing importance given the proliferation of ever tighter regulatory requirements. To address this, we developed a flowchart to guide the selection of contextually appropriate fairness measures. Twelve criteria were used to formulate the flowchart. This included consideration of model assessment criteria, model selection criteria, and data bias. We also review fairness literature in the context of machine learning and link it to core...
