The Effect of Ethical and Unethical AI Actions on Outcomes of Trust and Performance in Human-AI Teams
Beau G. Schelble, Caitlin Lancaster, Wen Duan, Rohit Mallick, Nathan J. McNeese, Jeremy Lopez
Proceedings of the 56th Hawaii International Conference on System Sciences (2023)
Abstract
This study improves the understanding of trust in human-AI teams by investigating the relationship of AI teammate ethicality on individual outcomes of trust (i.e., monitoring, confidence, fear) in AI teammates and human teammates over time. Specifically, a synthetic task environment was built to support a three-person team with two human teammates and one AI teammate (simulated by a confederate). The AI teammate performed either an ethical or unethical action in three missions, and measures of trust in the human and AI teammate were taken after each mission. Results from the study revealed that unethical actions by the AI had a significant effect on nearly all of the outcomes of trust measured and that levels of trust were dynamic over time for both the AI and human teammate, with the AI teammate recovering trust in Mission 1 levels by Mission 3. AI ethicality was mostly unrelated to participants' trust in their fellow human teammates but did decrease perceptions of fear, paranoia, and skepticism in them, and trust in the human and AI teammate was not significantly related to individual performance outcomes, which both diverge from previous trust research in human-AI teams utilizing competency-based trust violations.
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BibTeX
@inproceedings{schelble2023the,
title = {The Effect of Ethical and Unethical AI Actions on Outcomes of Trust and Performance in Human-AI Teams},
author = {Schelble, Beau G. and Lancaster, Caitlin and Duan, Wen and Mallick, Rohit and McNeese, Nathan J. and Lopez, Jeremy},
year = {2023},
booktitle = {Proceedings of the 56th Hawaii International Conference on System Sciences}
}Topics
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