Artificial Intelligence in the Government: Responses to Failures and Social Impact

Artificial Intelligence (AI) is pervading the government and transforming how public services are provided to consumers---from allocation of benefits to law enforcement, risk monitoring and the provision of services. Despite technological improvements, AI systems are fallible and may err. How do consumers respond when learning of AI's failures? In thirteen preregistered studies (N = 3,724), we document a robust effect of algorithmic transference: algorithmic failures are generalized more broadly than human failures. Rather than reflecting generalized algorithm aversion, algorithmic transference is rooted in social categorization: it stems from how people perceive a group of AI systems versus a group of humans---as outgroups characterized by greater homogeneity than ingroups of comparable humans. Because AI systems are perceived as more homogeneous than people, failure information about one AI algorithm is transferred to another algorithm at a higher rate than failure information about a person is transferred to another person. Assessing AI's impact on consumers and societies, we show how the premature or mismanaged deployment of faulty AI technologies may engender algorithmic transference and undermine the very institutions that AI systems are meant to modernize.