Trusting Autonomous Teammates in Human-AI Teams - A Literature Review
Wen Duan, Christopher Flathmann, Nathan J. McNeese, Matthew J. Scalia, Ruihao Zhang, Jamie C. Gorman, Guo Freeman, Shiwen Zhou, Allyson Ivy Hauptman, Xiaoyun Yin
Abstract
As autonomous AI agents become increasingly integrated into human teams, the level of trust humans place in these agents - both as a piece of technology and increasingly viewed as teammates - significantly impacts the success of human-AI teams (HATs). This work presents a literature review of the HAT research that investigates humans’ trust in their AI teammates. In this review, we first identify the ways in which trust was conceptualized and operationalized, which underscores the pressing need for clear definitions and consistent measurements. Then, we categorize and quantify the factors found to influence trust in an AI teammate, highlighting that agent-related factors (such as transparency, reliability) have the strongest impacts on trust in HAT research. We also identify under-explored factors related to humans, teams, and environments, and gaps for future HAT research and design.
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