Exploring Human Trust in Ai : The Influence of Ingroup/Outgroup Context and Ai Human-Likeness

Artificial intelligence (AI) agents are increasingly being used in various domains, and it is important to understand how human beings perceive and interact with these agents. In this study, we investigated the effects of ingroup/outgroup and human-likeness on trust in AI agents. We recruited 160 participants and randomly assigned them to one of four conditions: ingroup-humanoid AI, ingroup-non-humanoid AI, outgroup-humanoid AI, and outgroup-non-humanoid AI. We used a trust game to measure participants' levels of trust in the AI agents. Results showed that participants had higher levels of trust in the ingroup and humanoid AI conditions as compared to the outgroup and non-humanoid AI conditions. Additionally, there was a significant interaction effect between ingroup/outgroup and AI type on trust. These findings contribute to the understanding of how social categorization and the human-likeness of AI agents influence trust in humans. They have important implications for the development and design of AI technologies that interact with humans, particularly in areas where trust is crucial, such as healthcare and autonomous vehicles