@inproceedings{da-silva-ferreira-junior-etal-2025-generating,
    title = "Generating Impact and Critique Explanations of Predictions made by a Goal Recognizer",
    author = "da Silva Ferreira Junior, Jair  and
      Zukerman, Ingrid  and
      Makalic, Enes  and
      Paris, Cecile L.  and
      Vered, Mor",
    editor = "Flek, Lucie  and
      Narayan, Shashi  and
      Phương, L{\^e} Hồng  and
      Pei, Jiahuan",
    booktitle = "Proceedings of the 18th International Natural Language Generation Conference",
    month = oct,
    year = "2025",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.inlg-main.34/",
    pages = "554--575",
    abstract = "In this paper, we generate two types of explanations, Impact and Critique, of predictions made by a Goal Recognizer (GR) {--} a system that infers agents' goals from observations. Impact explanations describe the top-predicted goal(s) and the main observations that led to these predictions. Critique explanations augment these explanations with evidence that challenges the GR{'}s predictions if so warranted. Our user study compares users' goal-recognition accuracy for Impact and Critique explanations, and users' views about these explanations, under three prediction-correctness conditions: correct, partially correct and incorrect. Our results show that (1) users stick with a GR{'}s predictions, even when a Critique explanation highlights its flaws; yet (2) Critique explanations are deemed better than Impact explanations in most respects."
}