Benchmarking bias: Expanding clinical AI model card to incorporate bias reporting of social and non-social factors
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Abstract (arXiv)
Clinical AI model reporting cards should be expanded to incorporate a broad bias reporting of both social and non-social factors. Non-social factors consider the role of other factors, such as disease dependent, anatomic, or instrument factors on AI model bias, which are essential to ensure safe deployment.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 54 references that are in the map. First posted on arXiv 21 Nov 2023.
Builds on
12 of 54 references.
References · 12
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
- 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
- 2018Model Cards for Model Reportingcited by 1,080
- 2023Towards Generalist Biomedical AIcited by 289
- 2021Reading Race: AI Recognises Patient's Racial Identity In Medical Imagescited by 156
- 2020CheXclusion: Fairness gaps in deep chest X-ray classifierscited by 153
- 2020Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddingscited by 79
- 2022Improving the Fairness of Chest X-ray Classifierscited by 52
- 2021Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Reviewcited by 30
- 2021Measuring Model Biases in the Absence of Ground Truthcited by 24
- 2021A survey on shape-constraint deep learning for medical image segmentationcited by 16
- 2022Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methodscited by 15
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