ArticlePatterns (New York, N.Y.)2024
FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A framework for assessing algorithmic discrimination risks in training data: a case of pediatric type 1 diabetes.JAMIA open · 2026Article
- Attention guided fair artificial intelligence modeling for skin cancer diagnosis.NPJ digital medicine · 2026Article
- Dataset effects outweigh algorithmic effects in determining fairness of healthcare machine learning.NPJ digital medicine · 2026Article
- Fair and robust early readmission risk prediction from electronic health records via diffusion-based data augmentation and causal-invariant representation learning.Scientific reports · 2026Article
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Integration of fairness-awareness into clinical language processing models.Communications medicine · 2026Article
- Evaluation and improvement of algorithmic fairness for COVID-19 severity classification using Explainable Artificial Intelligence-based bias mitigation.JAMIA open · 2026Article
- Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention.Biomedicines · 2025Review
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
- Advancing ethical AI in healthcare through interpretability.Patterns (New York, N.Y.) · 2025Article
- Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics.Diagnostics (Basel, Switzerland) · 2024Article
- FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare.Patterns (New York, N.Y.) · 2024Article
- Disparity implications of machine-learning-based MTM eligibility criteria.Journal of the American Pharmacists Association : JAPhAArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness. We propose an interpretable framework, fairness-aware interpretable modeling (FAIM), to improve model fairness without compromising performance, featuring an interactive interface to identify a "fairer" model from a set of high-performing models and promoting the integration of data-driven evidence and clinical expertise to enhance contextualized fairness. We demonstrate FAIM's value in reducing intersectional biases arising from race and sex by predicting hospital admission with two real-world databases, the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) and the database collected from Singapore General Hospital Emergency Department (SGH-ED). For both datasets, FAIM models not only exhibit satisfactory discriminatory performance but also significantly mitigate biases as measured by well-established fairness metrics, outperforming commonly used bias mitigation methods. Our approach demonstrates the feasibility of improving fairness without sacrificing performance and provides a modeling mode that invites domain experts to engage, fostering a multidisciplinary effort toward tailored AI fairness.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.