Human or AI Advice? Examining Trust, Influence, and Responsibility in Ethically-Charged Human-AI Team Decision-Making
Beau G. Schelble, Christopher Flathmann, Heba Aly, Joseph B. Lyons, Nathan J. McNeese
Journal of Cognitive Engineering and Decision Making, 1-35 (2026)
Abstract
AI systems are rapidly being placed in positions where they can directly influence decisions that require a strong consideration of ethics. This article reports on an experiment in which humans worked with a teammate acting as an expert advisor with expert-level knowledge, who informed and influenced their ethical decision-making. The identity of this expert advisor was manipulated to be either human or AI, and the influence exerted by the expert advisor was manipulated to be either low or high. Further, participants completed four scenarios, each exploring a different ethically charged decision. Results indicated that working with an AI teammate expert advisor led participants to perceive significantly lower levels of stress and responsibility when making ethical decisions, but AI expert advisors were also perceived as performing worse than human expert advisors. Additionally, when expert advisors exerted high influence levels, participants felt significantly less stress during the decision-making process. Finally, the scenario and ethical decisions made by participants had pervasive effects on trust, trustworthiness, perceived performance, and perceived power for both human and AI expert advisors. Future research efforts must ensure that the use of AI expert advisors in human-AI teams does not reduce the responsibility humans bear when making various ethical decisions.
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BibTeX
@article{schelble2026human,
title = {Human or AI Advice? Examining Trust, Influence, and Responsibility in Ethically-Charged Human-AI Team Decision-Making},
author = {Schelble, Beau G. and Flathmann, Christopher and Aly, Heba and Lyons, Joseph B. and McNeese, Nathan J.},
year = {2026},
journal = {Journal of Cognitive Engineering and Decision Making},
note = {1-35},
doi = {10.1177/15553434261434613}
}Topics
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