Evaluating Visual Moral Dilemmas in Human and LLMs: Insights from the Moral Machine Experiment

Abstract This study examined how large language models (LLMs) respond to visual moral dilemmas in the context of autonomous vehicles (AVs) and compared their preferences to human baseline patterns established by the Moral Machine Experiment. It examined 44 LLM variants for 13 visual moral machine dilemmas, evaluated the LLM's initial responses to the moral machine, compared baseline data to human responses, and investigated unbiased prompts and interventions. The study found significant differences between LLMs and humans in four dimensions: Status (Cohen's d = 0.428, p =.014), Law (Cohen's d = 0.680, p