The Situation Awareness Framework for Explainable AI (SAFE-AI) and Human Factors Considerations for XAI Systems
Name
The Situation Awareness Framework for Explainable AI SAFE-AI and Human Factors Considerations for XAI Systems.pdf
Description
Published version
Size
2.37 MB
Format
Adobe PDF
Checksum (MD5)
f09f7ea517f17ea6cd20cb9b3ca62254
Author(s)
Sanneman, Lindsay
Shah, Julie A
Date Issued
June 22, 2022
Journal
International Journal of Human–Computer Interaction
Publisher
Taylor & Francis
Citation
Sanneman, L., & Shah, J. A. (2022). The Situation Awareness Framework for Explainable AI (SAFE-AI) and Human Factors Considerations for XAI Systems. International Journal of Human–Computer Interaction, 38(18–20), 1772–1788. https://doi.org/10.1080/10447318.2022.2081282
Version
Final published version
Abstract
Recent advances in artificial intelligence (AI) have drawn attention to the need for AI systems to beunderstandable to human users. The explainable AI (XAI) literature aims to enhance human under-standing and human-AI team performance by providing users with necessary information about AI sys-tem behavior. Simultaneously, the human factors literature has long addressed importantconsiderations that contribute to human performance, including how to determine human informa-tional needs, human workload, and human trust in autonomous systems. Drawing from the human fac-tors literature, we propose the Situation Awareness Framework for Explainable AI (SAFE-AI), a three-level framework for the development and evaluation of explanations about AI system behavior. Ourproposed levels of XAI are based on the informational needs of human users, which can be deter-mined using the levels of situation awareness (SA) framework from the human factors literature. Basedon our levels of XAI framework, we also suggest a method for assessing the effectiveness of XAI sys-tems. We further detail human workload considerations for determining the content and frequency ofexplanations as well as metrics that can be used to assess human workload. Finally, we discuss theimportance of appropriately calibrating user trust in AI systems through explanations along with othertrust-related considerations for XAI, and we detail metrics that can be used to evaluate user trust inthese systems.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/10447318.2022.2081282
