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

Authors

  • Jinsun Jung College of Nursing, Seoul National University Center for Human-Caring Nurse Leaders for the Future by Brain Korea 21 (BK 21) Four Project
  • Hyeoneui Kim College of Nursing, Seoul National University Center for Human-Caring Nurse Leaders for the Future by Brain Korea 21 (BK 21) Four Project The Research Institute of Nursing Science

DOI:

https://doi.org/10.1609/aaai.v38i21.30458

Keywords:

Explainable Artificial Intelligence, AI For Healthcare, Focus Group Interview

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.
AAAI-24 / IAAI-24 / EAAI-24 Proceedings Cover

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Published

2024-03-24

How to Cite

Jung, J., & Kim, H. (2024). Evaluating the Effectiveness of Explainable Artificial Intelligence Approaches (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23528-23529. https://doi.org/10.1609/aaai.v38i21.30458

Issue

Vol. 38 No. 21: IAAI-24, EAAI-24, AAAI-24 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Student Abstract and Poster Program