2026 DMKD On the Definition and Detection of Cherry-Picking in Counterfactual Explanations James Hinns, Sofie Goethals, Stephan Veeken, and 2 more authors Data Mining and Knowledge Discovery, 2026 Accepted for publication; forthcoming arXiv HTML IJCCI Exposing Shortcuts in Image Classification by Aggregating Counterfactuals James Hinns and David Martens In Computational Intelligence, 2026 DOI arXiv HTML CACM Cash or Comfort? How LLMs Value Your Inconvenience Mateusz Cedro, Timour Ichmoukhamedov, Sofie Goethals, and 3 more authors Communications of the ACM, 2026 Accepted for publication; forthcoming DOI HTML 2025 arXiv Aggregating Local Saliency Maps for Semi-Global Explainable Image Classification James Hinns and David Martens arXiv preprint arXiv:2506.23247, 2025 DOI HTML DSS Tell Me a Story! Narrative-Driven XAI with Large Language Models David Martens, James Hinns, Camille Dams, and 2 more authors Decision Support Systems, 2025 DOI arXiv HTML 2024 arXiv How Good Is My Story? Towards Quantitative Metrics for Evaluating LLM-Generated XAI Narratives Timour Ichmoukhamedov, James Hinns, and David Martens arXiv preprint arXiv:2412.10220, 2024 DOI HTML 2021 PRICAI An Initial Study of Machine Learning Underspecification Using Feature Attribution Explainable AI Algorithms: A COVID-19 Virus Transmission Case Study James Hinns, Xiuyi Fan, Siyuan Liu, and 3 more authors In PRICAI 2021: Trends in Artificial Intelligence, 2021 DOI HTML