James Hinns

Human-centred interpretability researcher working on explainable and safer AI in the Applied Data Mining Research Group.

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I am a final-year PhD candidate in Explainable AI at the University of Antwerp. I will soon join KU Leuven as a postdoc in Human-Centred XAI.

My work focuses on human-centred interpretability methods, explanation evaluation, and machine learning systems that are more understandable, trustworthy, and aligned with human needs.

news

June 2026 Successfully defended my thesis, Human-Centred Approaches for Generating and Evaluating Explainable AI Across Modalities, at my internal PhD defence. I will proceed to the public defence in September 2026.
June 2026 Our article, On the Definition and Detection of Cherry-Picking in Counterfactual Explanations, was accepted through the ECML PKDD 2026 Journal Track. It will appear in Data Mining and Knowledge Discovery and be presented at ECML PKDD in September 2026.
February 2026 Our article, Cash or Comfort? How LLMs Value Your Inconvenience, was accepted for publication in Communications of the ACM.
February 2026 Started a research stay at NYU Stern, hosted by Prof. Foster Provost, continuing until May 2026.

latest posts

selected publications

  1. 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
  2. Exposing Shortcuts in Image Classification by Aggregating Counterfactuals
    James Hinns and David Martens
    In Computational Intelligence, 2026
  3. Aggregating Local Saliency Maps for Semi-Global Explainable Image Classification
    James Hinns and David Martens
    arXiv preprint arXiv:2506.23247, 2025
  4. 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