Artificially intelligent agents are increasingly moving beyond decision-support roles to become teammates, creating novel team configurations beyond traditional human-AI dyads. One such configuration is a hierarchical team, where a human leader directs both human and agent subordinates. This raises key questions about managing mixed-identity subordinates and about how agent traits (ability/integrity) shape trust. We present a lab study with teams of four (one human leader, with one human and two agent subordinates) performing a collaborative block-moving task. Leaders interacted with three types of agents that varied in ability and integrity: High-Integrity-High-Ability (HI-HA), High-Integrity-Low-Ability (HI-LA), and Low-Integrity-High-Ability (LI-HA). Leaders generally preferred and maintained stable trust in humans, whereas trust in agents declined significantly under both low-ability and low-integrity conditions, with stronger sensitivity to integrity. Thematic analysis revealed distinct expectations tied to identity: leaders granted humans an inherent baseline of trust due to humans’ adaptability, while evaluating agents primarily on task efficiency and obedience.

Human interaction with autonomous technologies has been extensively studied, mostly focusing on one-to-one dyadic interactions. In contrast, this study examines human altruistic and compliance behaviors in multiple-operator single-agent (MOSA) interaction. We developed a testbed where multiple players perform an evacuation task, assisted by an AI agent that plans the optimal route for everyone. During the evacuation, players could exhibit altruism by reporting additional information, albeit at a personal cost. A lab study with 32 participants, each completing four trials under varying display configurations that manipulated the communication of altruistic actions, yielded 1,012 and 3,865 data points on altruism and compliance, respectively. Using mixed-effects logistic regression, we identified key predictors of altruistic and compliance behaviors and developed prediction models, with accuracies of 73.36% and 91.07%, respectively. These findings offer valuable insights into the role of information transparency, reciprocal altruism, and compliance in MOSA interaction, with implications for designing AI-assisted collaborative systems.

A particular concern with SAE Level 3 automation is the takeover transition from the automated vehicle to the human driver. In response, research has focused on investigating this transition. However, researchers have used a wide range of metrics to measure takeover performance. The lack of consistency in these metrics poses challenges for synthesizing findings. To address this issue, we conducted a systematic literature review of studies published between January 2009 and December 2019, focusing on the takeover performance metrics. Following prior research, we categorize these metrics into two dimensions: timeliness and quality. Additionally, we summarize the scenarios used to elicit takeover requests and analyze the corresponding maneuvers (braking, lane changing, and lane keeping). The results have shown inconsistencies in calculation and naming conventions of takeover performance metrics. Based on these findings, this study proposes several directions for standardizing definitions and terminology, and advancing toward a unified measure of takeover performance.

This study aims to explore the associations between individuals’ trust dynamics in automated/autonomous technologies and their personal characteristics, and to further examine whether personal characteristics can be used to predict a user’s trust dynamics type. The experimental data involved 130 participants who performed a simulated surveillance task that consisted of a compensatory tracking task and a threat detection task. An imperfect automated threat detector assisted participants in the detection task. Using a pre-experimental survey covering 12 constructs and 28 dimensions, we collected data on participants’ personal characteristics. Based on the experimental data, we performed k-means clustering and identified three trust dynamics types. Subsequently, we conducted one-way analyses of variance to evaluate differences among the three trust dynamics types in terms of personal characteristics, behaviors, performance, and postexperimental ratings. Participants were clustered into three groups, namely Bayesian decision makers, disbelievers, and oscillators. Results showed that the clusters differ significantly in seven personal characteristics: masculinity, positive affect, extraversion, neuroticism, intellect, performance expectancy, and high expectations. The disbelievers tend to have highneuroticismand lowperformance expectancy. The oscillators tend to have higher scores inmasculinity,positive affect,extraversion, andintellect. We also found significant differences in behaviors, performance, and postexperimental ratings across the three groups. The disbelievers are the least likely to blindly follow the recommendations made by the automated threat detector. Based on the significant personal characteristics, we developed a decision tree model to predict the trust dynamics type with an accuracy of 70% . This model offers promising implications for identifying individuals whose trust dynamics may deviate from a Bayesian pattern.