Title: From comparison to collaboration: Experimental evidence on human and artificial intelligence in public decision making
Abstract: This study investigates nuanced public perceptions of artificial intelligence (AI) in public sector decision-making, extending existing scholarship by examining human–AI collaboration and its various configurations across diverse scenarios. Using a data-centric categorization of decision scenarios and drawing on two experiments in China: a vignette experiment (Study 1; n = 611) and a conjoint experiment (Study 2; n = 894), this study yields two main findings. First, while human decision-making is perceived as more acceptable than AI, AI is seen as more efficient, and human decision-making is viewed as fairer. Notably, human–AI collaboration tends to achieve high public acceptance by leveraging the complementary strengths of both humans and AI across different decision scenarios. Second, the specific configuration of human–AI collaboration significantly influences public perceptions, with collaborative approach, outcome explanation, responsibility attribution, and stakeholder involvement playing critical roles. This study not only provides a data-centric perspective on decision scenarios but also advances our understanding of human–AI collaboration in decision-making, offering practical insights for optimizing its configurations.
Keywords: Public perception; Artificial intelligence; Human–AI collaboration; Public sector; Decision-making
DOI: 10.1016/j.im.2026.104364
