Analyzing the Ethical Logic of Eight Large Language Models

This study examines the expressed ethical logic of eight prominent large language models from OpenAI, Meta, Perplexity, Anthropic, Google, Mistral, DeepSeek, and xAI. Each model answered direct questions about its ethical principles and responded to five classic moral dilemmas. Responses were analyzed using the consequentialist/deontological distinction, Moral Foundations Theory, and Kohlbergs stages of moral development. Across models, ethical judgments were broadly convergent and typically emphasized harm minimization, fairness, and contextual qualification. The models nevertheless differed in their willingness to decide, the rationales used to defend choices, and the relative weight assigned to rules, outcomes, role obligations, and interpersonal considerations. Their self-descriptions were erudite, cautious, and strongly shaped by a conversational persona. The analysis of self-reports has been central to the study of human psychology and communication. We propose, with appropriate cautions, it can enhance our understanding of how artificial intelligence works and how it may be able to augment human ethical behavior