ML Anthology

Evaluating the Effectiveness of Explainable Artificial Intelligence Approaches (Student Abstract)

Jinsun Jung, Hyeoneui Kim
AAAI 2024 pp. 23528-23529
doi:10.1609/AAAI.V38I21.30458 /aaai/2024/jung2024aaai-evaluating/

Abstract

Explainable Artificial Intelligence (XAI), a promising future technology in the field of healthcare, has attracted significant interest. Despite ongoing efforts in the development of XAI approaches, there has been inadequate evaluation of explanation effectiveness and no standardized framework for the evaluation has been established. This study aims to examine the relationship between subjective interpretability and perceived plausibility for various XAI explanations and to determine the factors affecting users' acceptance of the XAI explanation.

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Cite

Text

Jung and Kim. "Evaluating the Effectiveness of Explainable Artificial Intelligence Approaches (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30458

Markdown

[Jung and Kim. "Evaluating the Effectiveness of Explainable Artificial Intelligence Approaches (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/jung2024aaai-evaluating/) doi:10.1609/AAAI.V38I21.30458

BibTeX

@inproceedings{jung2024aaai-evaluating,
  title     = {{Evaluating the Effectiveness of Explainable Artificial Intelligence Approaches (Student Abstract)}},
  author    = {Jung, Jinsun and Kim, Hyeoneui},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23528-23529},
  doi       = {10.1609/AAAI.V38I21.30458},
  url       = {https://mlanthology.org/aaai/2024/jung2024aaai-evaluating/}
}