Lexical Anthropomorphization Influences on Moral Judgments of AI Bad Behavior

TL;DR

This study investigates how humanizing language about AI influences moral judgments, finding minimal effects except that anthropomorphic primes can increase perceptions of dishonesty.

Contribution

It provides empirical evidence that lexical anthropomorphism has limited impact on moral evaluations of AI behavior across various contexts.

Findings

Anthropomorphic language slightly increased perceptions of AI dishonesty.

Type of moral violation was the strongest predictor of moral judgment.

Design cues had little influence on moral assessments of AI misbehavior.

Abstract

Anthropomorphic language describing artificial intelligence (AI) is widespread in media, policy, and everyday discourse; so too are discussions of AI bad behavior, from hallucinations to inappropriate comments. How does humanizing language about AI shape moral judgments when AI behaves badly? Across four experiments (total N = 1,020), we tested whether lexical anthropomorphism (LA) primes shape judgments of AI moral character, behavior morality, and behavioral responsibility. Studies 1-3 tested interactions between anthropomorphic language and humanizing design cues (icons, names, self-referencing) in the context of amoral errors. Study 4 extended this to genuinely immoral AI behavior across seven moral-violation types. Results indicate humanizing language and design cues have little influence on moral judgments of misbehaving AI. Where effects emerged, high-anthropomorphic primes elevated perceptions of an AI's capacity for dishonesty. The type of moral violation observed was the strongest predictor of moral judgments, with harm and degradation violations producing the broadest negative character assessments. Prime drift, horn effects, and egoistic value orientations emerged as potentially important predictors of AI moral judgments.