Safe, Ethical, Certified, Uncertainty-Aware, Robust, and Explainable AI for Health

Beschreibung

This book constitutes the refereed post-conference proceedings of the First International Workshop, on Safe, Ethical, Certified, Uncertainty-aware, Robust, and Explainable AI for Health, SECURE AI4H@AAAI 2026, Held in Conjunction with AAAI 2026, held in Singapore, during January 27, 2026.

The 16 full papers and 3 short papers included in this volume were carefully reviewed and selected from 60 submissions. The papers cover the following topical sections: Uncertainty, Calibration, and Reliable Prediction; Robustness, Fairness, and Explainability in Clinical Models; Trustworthy Large Language Models and Agents in Health; Governance, Security, and Deployment.

Autor*in

Hong Qin

Themen in »Safe, Ethical, Certified, Uncertainty-Aware, Robust, and Explainable AI for Health«

Trustworthy AI AI safety in healthcare uncertainty quantification robust machine learning explainable AI fairness and bias mitigation privacy-preserving machine learning clinical AI evaluation regulation and certification multimodal health AI

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