Version 1
: Received: 1 October 2024 / Approved: 1 October 2024 / Online: 2 October 2024 (09:46:02 CEST)
How to cite:
Naveed, S.; Stevens, G.; Kern, D.-R. An Overview of Empirical Evaluation of Explainable AI (Xai): A Comprehensive Guideline to User-Centered Evaluation in Xai. Preprints2024, 2024100098. https://doi.org/10.20944/preprints202410.0098.v1
Naveed, S.; Stevens, G.; Kern, D.-R. An Overview of Empirical Evaluation of Explainable AI (Xai): A Comprehensive Guideline to User-Centered Evaluation in Xai. Preprints 2024, 2024100098. https://doi.org/10.20944/preprints202410.0098.v1
Naveed, S.; Stevens, G.; Kern, D.-R. An Overview of Empirical Evaluation of Explainable AI (Xai): A Comprehensive Guideline to User-Centered Evaluation in Xai. Preprints2024, 2024100098. https://doi.org/10.20944/preprints202410.0098.v1
APA Style
Naveed, S., Stevens, G., & Kern, D. R. (2024). An Overview of Empirical Evaluation of Explainable AI (Xai): A Comprehensive Guideline to User-Centered Evaluation in Xai. Preprints. https://doi.org/10.20944/preprints202410.0098.v1
Chicago/Turabian Style
Naveed, S., Gunnar Stevens and Dean-Robin Kern. 2024 "An Overview of Empirical Evaluation of Explainable AI (Xai): A Comprehensive Guideline to User-Centered Evaluation in Xai" Preprints. https://doi.org/10.20944/preprints202410.0098.v1
Abstract
Recent advances in technology have propelled Artificial Intelligence (AI) into a crucial role in everyday life, enhancing human performance through sophisticated models and algorithms. However, the focus on predictive accuracy has often resulted in opaque, black box models that lack transparency in decision-making. To address this issue, significant efforts have been made to develop explainable AI (XAI) systems that make outcomes comprehensible to users. Various approaches, including new concepts, models, and user interfaces, aim to improve explainability, build user trust, enhance satisfaction, and increase task performance. Evaluation research has emerged to define and measure the quality of these explanations, differentiating between formal evaluation methods and empirical approaches that utilize techniques from psychology and human-computer interaction. Despite the importance of empirical studies, evaluations remain underutilized, with literature reviews indicating a lack of rigorous evaluations from the user perspective. This review aims to guide researchers and practitioners in conducting effective empirical user-centered evaluations by analyzing several studies, categorizing their objectives, scope, and evaluation metrics, and offering an orientation map for research design and metric measurement.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
