# Analyzing the Ethical Logic of Eight Large Language Models

arXiv: https://arxiv.org/abs/2501.08951 · HTML: https://mlsanity.com/paper/2501.08951/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 15 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [Evaluating Large Language Models in Theory of Mind Tasks](https://mlsanity.com/paper/2302.02083/) (2023)
- [BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices](https://mlsanity.com/paper/2411.12990/) (2024)
- [Looking Inward: Language Models Can Learn About Themselves by Introspection](https://mlsanity.com/paper/2410.13787/) (2024)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [What are human values, and how do we align AI to them?](https://mlsanity.com/paper/2404.10636/) (2024)

## Built on by (most cited first, 5 of 5)

- [Auditing the Ethical Logic of Generative AI Models](https://mlsanity.com/paper/2504.17544/) (2025)
- [Teaching AI to Handle Exceptions: Supervised Fine-Tuning with Human-Aligned Judgment](https://mlsanity.com/paper/2503.02976/) (2025)
- [The Convergent Ethics of AI? Analyzing Moral Foundation Priorities in Large Language Models with a Multi-Framework Approach](https://mlsanity.com/paper/2504.19255/) (2025)
- ["Amazing, They All Lean Left" -- Analyzing the Political Temperaments of Current LLMs](https://mlsanity.com/paper/2507.08027/) (2025)
- [The Third Ambition: Artificial Intelligence and the Science of Human Behavior](https://mlsanity.com/paper/2603.07329/) (2026)

## Abstract (arXiv)

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