Gender Differences and Social Design in Human-AI Collaboration: Insights from Virtual Cobot Interactions Under Varying Task Loads

This work explores the effects of users’ gender and social design features of AI under different task load conditions on human-like attributions, social impact, work performance and perceived workload, user experience, and various other measures in Human-AI Interaction (HAII). Users had to execute sorting and dispatch tasks in collaboration with a virtual cobot. The degree of social gestalt of the cobot was varied by the ability to make small talk (i.e., talkative vs. non-talkative cobot), and the task load was increased by adding a secondary task (i.e., high vs. low task load condition). Overall, the talkative cobot led to a more positive perception of the cobot and increased social qualities like sense of meaning and team membership compared to the non-talkative cobot. The following gender effect was particularly interesting. The talkative cobot had a buffering effect for women and a distraction conflict effect for men in high task load conditions. When interacting with the talkative robot, women find the high task condition less stressful. In contrast thereto, the talkative cobot was distracting for men in the high task load condition. Our results highlight that social design choices and interindividual differences influence a successful collaboration between humans and AI. The work also shows the added value of systematic XR-simulations for the investigation and design of human-centered HAIIs (eXtended AI approach).