A Process for Evaluating Explanations for Transparent and Trustworthy AI Prediction Models. - Erhan Pisirir; Jared M. Wohlgemut; Evangelia Kyrimi; Rebecca S. Stoner; Zane B. Perkins; Nigel R. M. Tai; D. William R. Marsh (ICHI, 2023)

Explainable AI: Definition and attributes of a good explanation for health AI. - Evangelia Kyrimi; Scott McLachlan; Jared M. Wohlgemut; Zane B. Perkins; David A. Lagnado; William Marsh 001; ExAIDSS Expert Group (CoRR, 2024)

Updating and recalibrating causal probabilistic models on a new target population. - Evangelia Kyrimi; Rebecca S. Stoner; Zane B. Perkins; Erhan Pisirir; Jared M. Wohlgemut; William Marsh 001; Nigel R. M. Tai (J. Biomed. Informatics, 2024)

Presenting predictions and performance of probabilistic models for clinical decision support in trauma care. - Cansu Alptekin; Jared M. Wohlgemut; Zane B. Perkins; William Marsh 001; Nigel R. M. Tai; Barbaros Yet (Int. J. Medical Informatics, 2025)

Counterfactual reasoning using causal Bayesian networks as a healthcare governance tool. - Evangelia Kyrimi; Somayyeh Mossadegh; Jared M. Wohlgemut; Rebecca S. Stoner; Nigel R. M. Tai; William Marsh 001 (Int. J. Medical Informatics, 2025)

Explainable AI: definition and attributes of a good explanation for health AI. - Evangelia Kyrimi; Scott McLachlan; Jared M. Wohlgemut; Zane B. Perkins; David A. Lagnado; William Marsh 001; Alexander Gimson; Ali Shafti; Ari Ercole; Amitava Banerjee; Ben Glocker; Burkhard Schafer 001; et al. (AI Ethics, 2025)