What Counts Underlying LLMs’ Moral Dilemma Judgments?

AbstractMoral judgments in LLMs increasingly capture the attention of researchers in AI ethics domain. This study explores moral judgments of three open-source large language models (LLMs)—Qwen-1.5-14B, Llama3-8B, and DeepSeek-R1 in plausible moral dilemmas, examining their sensitivity to social exposure and collaborative decision-making. Using a dual-process framework grounded in deontology and utilitarianism, we evaluate LLMs’ responses to moral dilemmas under varying social contexts. Results reveal that all models are significantly influenced by moral norms rather than consequences, with DeepSeek-R1 exhibiting a stronger action tendency compared to Qwen-1.5-14B and Llama3-8B, which show higher inaction preferences. Social exposure and collaboration impact LLMs differently: Qwen-1.5-14B becomes less aligned with moral norms under observation, while DeepSeek-R1’s action tendency is moderated by social collaboration. These findings highlight the nuanced moral reasoning capabilities of LLMs and their varying sensitivity to social cues, providing insights into the ethical alignment of AI systems in socially embedded contexts.