The study, by researchers at Carnegie Mellon University, is published in Computers in Human Behavior. The article is part of a special issue, "The Social Bridge: An Interdisciplinary View on Trust in Technology," in which researchers from a range of disciplines explore mechanisms and functions of trust in people and technologies.
"Our findings challenge traditional concerns that AI-driven management fosters distrust and demonstrate a path by which AI complements human work by providing greater transparency and alignment with workers’ expectations," suggests Anita Williams Woolley, Professor of Organizational Behavior at Carnegie Mellon’s Tepper School of Business, who co-authored the study. "The results have broad implications for AI-powered performance management in industries increasingly reliant on digital and algorithmic work environments."
Applications of machine learning and AI have consistently proven capable of performing demanding cognitive tasks, provided they can be routinized. But in non-routine work, AI capabilities (e.g., those designed to facilitate managers’ ability to monitor productivity) often backfire, fostering enmity instead of efficiency.
In this study, researchers sought to determine how the frequency of feedback and the uncertainty of a task interacted to influence workers’ perceptions of an algorithm’s trustworthiness. In a randomized, controlled experiment, 140 men and women (primarily White and with a median age of 39) performed caregiving tasks in an online, simulated home healthcare environment.
Individuals were randomly assigned to receive or not receive automated real-time feedback (i.e., feedback delivered during the task) while performing their work under conditions of high or low uncertainty. After completing the task, they received an algorithmically determined rating based on their actual performance on the task.
Real-time feedback increased the perceived trustworthiness of the performance rating by boosting workers’ sense of their own work quality (i.e., knowledge of the results) and reducing the degree to which they were surprised by their final evaluation. This, in turn, enhanced workers’ trust in AI-generated performance ratings - particularly in non-routine work settings where uncertainty was high.
Among the study's limitations, the authors note that their findings may not generalize to all circumstances, in part because study participants were not drawn from a population of caregivers and the simulated task did not represent actual caregiving. In addition, the study did not examine the role of individual differences, such as levels of conscientiousness and expertise.
"Non-routine work has long posed challenges to traditional management strategies, and the development of algorithmic management systems offers an opportunity to begin to address them," notes Allen S. Brown, a PhD student in Organizational Behavior and Theory at Carnegie Mellon's Tepper School of Business, who led the study. "Our identification of a new framework for examining managerial interventions, one that makes performance standards more transparent and increases workers' knowledge of the results, is particularly relevant in today’s emerging work environments."
The study was funded by the AI-CARING Project of the U.S. National Science Foundation.
Allen S Brown, Christopher R Dishop, Andrew Kuznetsov, Ping-Ya Chao, Anita Williams Woolley.
Beyond efficiency: Trust, AI, and surprise in knowledge work environments.
Computers in Human Behavior, Volume 167, 2025. doi: 10.1016/j.chb.2025.108605