The silicon service spectrum: warmth and competence explain people’s preferences for AI assistants

Abstract

Introduction: The past year has seen the rise of many variants of large language model chatbots that all attempt to carry out verbal tasks requested by users. These chatbots perform various collaborative tasks, such as brainstorming, question and answering, summarization, and holding other forms of conversations, embedding themwithin our daily society. As these AI assistants become increasingly integrated into societal structures, understanding people’s perceptions toward them offers insights into how to better facilitate that integration, and how different our current understanding of human-human interactions parallels human-AI interactions. This project explores people’s preferences toward responses generated by various chatbots. Methods: Leveraging a comprehensive dataset composed of thousands of pairwise comparisons of responses from 17 popular chatbots, we applied multidimensional scaling (MDS) and property fitting (PROFIT) methodologies to uncover the dimensionality of why some models are similarly or dissimilarly preferred on average by people. Results: In line with previous research on universal dimensions of social cognition, interactions with chatbots are predominantly perceived along two dimensions: warmth and competence. Also similar to social cognition applied to humans, the dimensions displayed a curvilinear trend where the highest levels of default warmth are found in models with moderate levels of competence. Models at extremely high and extremely low levels of competence tended to have lower levels of default warmth. Discussion: This research advances our understanding of the interface between technology and social psychology. As chatbots and AI become increasingly prevalent within societal interactions, we see that many of the same principles found in perceptions between humans can also apply to AI.