Effects of Personalization on Credit and Blame for AI-Generated Content: Evidence from Four Countries

Abstract

Generative artificial intelligence (AI) raises ethical questions concerning moral and legal responsibility—specifically, the attributions of credit and blame for AI-generated content. For example, if a human invests minimal skill or effort to produce a beneficial output with an AI tool, can the human still take credit? How does the answer change if the AI has been personalized (i.e., fine-tuned) on previous outputs produced (i.e., without AI assistance) by the same human? We conducted pre-registered experiments with representative samples (N = 1,802) from four countries (US, UK, China, and Singapore). We investigated laypeople’s attributions of credit and blame to human users for producing beneficial or harmful outputs with a standard large language model (LLM), a personalized LLM, and without AI assistance (control condition). Participants generally attributed more credit to human users of personalized versus standard LLMs for beneficial outputs, whereas LLM type did not significantly affect blame attributions for harmful outputs, with a partial exception among Chinese participants. In addition, UK participants attributed more blame for using any type of LLM versus no LLM. Practical, ethical, and policy implications of these findings are discussed.

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