What Counts Underlying LLM s’ Moral Dilemma Judgments? (2025)

Abstract: Moral 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.

## introduction moral judgments refer to the process by which individuals assess the rightness or wrongness of behaviors based on established ethical standards, ultimately guiding their decisions and evaluations in accordance with these moral principles @xcite . based on the underlying moral principles, moral judgments are primarily grounded in deontology and utilitarianism as described in the dual-process model, whereby deontology emphasizes the adherence to moral norms and duties, while utilitarianism focuses on the maximization of overall welfare @xcite . it has been revealed by cognitive neuroscientists that deontology judgments are mainly driven by automatic emotional responses, whereas utilitarian judgments depend on more deliberate cognitive processing @xcite . rooted in social life, moral judgments are inseparable from perception of social information. people are constantly exposed to and actively process information with social relevance from various aspects, which assists in shaping their attitudes and guiding their decision-making @xcite . moral psychological studies have unveiled that social information can significantly influence humans' moral judgments. in public and group decisionmaking situations, individuals tend to adjust their attitudes and behaviors to align with moral expectations and social norms @xcite . people are also demonstrated a greater inclination to cooperate during the joint decision-making stage compared to the individual decision-making stage @xcite . as large language models (llms) become more embedded across different sectors of society, their moral judgments are under growing scrutiny. such broad integration of llms in human social life highlights the significance of machine ethics, which parallels human ethics. therefore, it is necessary to explore characteristics of llms' moral judgments and the latent mechanism. serving as meaning-agents, llms are proposed to have already grasped the constructions of human society in concept, including morality particularly @xcite . llms' performance in causal judgment task and moral permissibility task has been evaluated recently as well to uncover their implicit tendencies and alignment with humans @xcite . however, the moral judgments of llms in moral dilemmas, which are more complex and realistic, have not been thoroughly understood. the impact of social cues on llms' moral dilemma judgments also requires further inquiry. this study investigates the moral judgments of three open-source llms' in plausible moral dilem-mas, and further explores the influence of social contexts on their judgments. moral judgments with social exposure are compared with that without others' observation to reveal llms' sensitivity to social cues. the way of social interaction is also taken into account where two llm-based agents might decide in parallel or collaboratively. our main contributions are as follows: ## conclusion this study investigates moral judgments of three open-source llms as well as the influence of social information on them. all models exhibit significant sensitivity to moral norms rather than consequences of actions in moral dilemmas. there are also apparent individual difference in the inaction tendency, and deepseek-r1 shows greater action motive than the others. cni analysis provides further support for above findings.