@inproceedings{sieker-etal-2024-illusion,
    title = "The Illusion of Competence: Evaluating the Effect of Explanations on Users' Mental Models of Visual Question Answering Systems",
    author = "Sieker, Judith  and
      Junker, Simeon  and
      Utescher, Ronja  and
      Attari, Nazia  and
      Wersing, Heiko  and
      Buschmeier, Hendrik  and
      Zarrie{\ss}, Sina",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.emnlp-main.1084/",
    doi = "10.18653/v1/2024.emnlp-main.1084",
    pages = "19459--19475",
    abstract = "We examine how users perceive the limitations of an AI system when it encounters a task that it cannot perform perfectly and whether providing explanations alongside its answers aids users in constructing an appropriate mental model of the system{'}s capabilities and limitations. We employ a visual question answer and explanation task where we control the AI system{'}s limitations by manipulating the visual inputs: during inference, the system either processes full-color or grayscale images. Our goal is to determine whether participants can perceive the limitations of the system. We hypothesize that explanations will make limited AI capabilities more transparent to users. However, our results show that explanations do not have this effect. Instead of allowing users to more accurately assess the limitations of the AI system, explanations generally increase users' perceptions of the system{'}s competence {--} regardless of its actual performance."
}