Hugging Face Trending Papers
Fairness Metrics based on the AUC in Clinical Predictive AI
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arXiv AI
Sep 1
Collapsibility of Performance Metrics in Clinical Predictive AI
arXiv:2608.30568v1 Announce Type: cross Abstract: Background: Population level assessments of predictive artificial intelligence (AI) can conceal performance disparities across subgroups. Fairness ev...
arXiv Machine Learning
Oct 3
Beyond Demographic Balance: Multi-Metric and Intersectional Evaluation of Fairness in MIMIC-IV Mortality Prediction
The paper examines how fairness conclusions in ICU mortality prediction using MIMIC-IV depend on the choice of metrics and the granularity of demographic analysis. It compares predictive-utility and subgroup-error metrics across various fairness interventions and introduces a lightweight adaptation strategy that balances ethnicity, gender, and insurance representation without conditioning on mortality outcomes. The study finds that different interventions can be evaluated differently across accuracy, sensitivity, and false-positive rate, and that marginal demographic summaries may hide heterogeneous error patterns within intersectional subgroups.
arXiv Machine Learning
Jul 13
FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness
arXiv:2607. 08953v1 Announce Type: new Abstract: Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes.
arXiv AI
Jun 2
Ethical Fairness in Ubiquitous Health Sensing without Known Attributes
arXiv:2603. 13373v4 Announce Type: replace-cross Abstract: In ubiquitous and mobile health systems, computational models infer human states from wearable, behavioral, and physiological sensing data.
arXiv Machine Learning
Oct 2
Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting
The paper investigates demographic bias in large language model (LLM) predictions of ICU mortality and introduces Case-Based Prompting (CAP), a training‑free method that adds clinically similar historical cases as contextual evidence. CAP improves predictive performance on the MIMIC‑IV dataset, raising AUROC from 0.806 to 0.873 and AUPRC from 0.497 to 0.694, while reducing subgroup disparities for sex and White‑Black comparisons. The study also shows that simply removing demographic information does not uniformly enhance fairness or accuracy, and that feature‑dependence patterns remain consistent across groups.
arXiv AI
Aug 20
FairGlucose: A CGM Fairness Benchmark Reveals Subgroup Disparities Hidden in Population-Level Validation
FairGlucose is a 300‑patient CGM cohort balanced across 12 demographic strata, providing 132,480 forecasting samples and 3,945 behavioral events. Benchmarking 33 models on 2‑hour glucose forecasting revealed that population‑level validation masks significant subgroup disparities, with error ratios ranging from 0.8 to 1.4 and T1D patients experiencing 6 mg/dL higher error than T2D. The study shows that these gaps persist across all models, align with clinically hard cases, and vary with input‑length sensitivity, underscoring the need for subgroup‑disaggregated reporting in digital health AI.