Socially (un)acceptable errors of AI: Consumer perceptions of different AI-induced errors
研究区分了AI的技术错误和社会错误，发现严重错误都会引发负面反应，但轻微社会错误在自我学习AI中可能加剧对少数群体的污名化，对消费者行为研究者和AI开发者有参考价值。
Artificial intelligence (AI) commonly errs in practice. This study investigates consumer responses to two distinct types of errors: technical errors stemming from technological disruptions in algorithmic processes and social errors, which involve violations of social norms. These distinctions are critical, as our research reveals different consumer response patterns based on error type and error severity. Grounded in the theory of mind perception and expectation disconfirmation theory, we present findings from multiple experiments demonstrating that severe errors, regardless of type, evoke negative consumer responses. In contrast, minor social errors seem anticipated and mostly elicit responses more akin to those for error-free AI performance. However, in the realm of self-learning AI, these minor social errors are problematic. They can perpetuate the stigmatization of minorities and ethnic groups, highlighting the urgent need to prevent AI from violating social norms.