As AI systems increasingly provide legal advice, questions arise regarding whether laypeople accept guidance from algorithms, especially when such advice is legally correct but socially controversial.
This paper reports a preregistered survey experiment involving 3,348 adults in mainland China, examining how individuals evaluate identical legal advice attributed either to an AI system or a human lawyer, and whether reasoning is provided.
Contrary to expectations of algorithm aversion, attribution to an AI system has no significant effect on perceived reasonableness. Mediation analyses reveal opposing psychological pathways underlying this null result.
AI-attributed advice is seen as more objective, which boosts perceived reasonableness, but is also viewed as less comprehensive and less attentive to special circumstances, which diminishes perceived reasonableness.
In contrast, providing legal reasoning significantly enhances perceived reasonableness regardless of source, largely by increasing perceptions of objectivity.
Qualitative responses confirm the tension between objectivity and contextual sensitivity in evaluations of legal advice. Together, these findings suggest that public responses to AI legal advisors are not shaped by rigid attitudes toward automation but by balancing competing normative expectations.
The results have implications for theories of algorithm aversion and the design of AI recommendation systems in normatively salient domains.
Blogger's Review: This study reveals the complex psychology behind public acceptance of AI legal advice, highlighting the importance of considering both objectivity and contextual sensitivity in AI system design. It offers a fresh perspective on how humans evaluate and respond to automated recommendations, warranting further exploration of its implications in legal practice.