Once One Fails, All Are Suspect: Understanding Error Generalization in AI

Abstract

Artificial intelligence (AI) systems are increasingly deployed in consumer-facing domains, where their occasional errors raise important questions about human responses. While prior research has examined trust and moral judgment following AI errors, little is known about how such errors generalize to perceptions of other AI systems and the mechanisms that drive this process. To address this gap, we conducted four one-factor experiments across distinct contexts. Results consistently show that AI errors elicit broader error generalization than comparable human errors. This effect appears to stem from perceptions that AI lacks flexibility and the capacity to learn from errors. These findings highlight the psychological asymmetry in how people interpret AI versus human errors and underscore the need for human-AI interaction research to consider the generalization effects of a single AI error on perceptions of other systems, which may ultimately affect user engagement and technology adoption.