Journal Article

LLM ethics benchmark: a three-dimensional assessment system for evaluating moral reasoning in large language models

Chen, Kevin, Atkinson, David, Jiao, Junfeng, Murali, Abhejay, Afroogh, Saleh, Dhurandhar, Amit

2025

Overview

This study establishes a novel framework for systematically evaluating the moral reasoning capabilities of large language models (LLMs) as they increasingly integrate into critical societal domains. Current assessment methodologies lack the precision needed to evaluate nuanced ethical decision-making in AI systems, creating significant accountability gaps. Our framework addresses this challenge by quantifying alignment with human ethical standards through three dimensions: foundational moral principles, reasoning robustness, and value consistency across diverse scenarios. This approach enables precise identification of ethical strengths and weaknesses in LLMs, facilitating targeted improvements and stronger alignment with societal values. To promote transparency and collaborative advancement in ethical AI development, we are publicly releasing both our benchmark datasets and evaluation codebase at https://github.com/The-Responsible-AI-Initiative/LLM_Ethics_Benchmark.git .

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/705/1042 / 639/705/258 / AI alignment / Artificial intelligence / Artificial Intelligence - ethics / Benchmark datasets / Benchmarking / Cognition & reasoning / Decision making / Decision Making - ethics / Ethics / Humanities and Social Sciences / Humans / Language / Large Language Models / LLM / Moral reasoning / Morals / multidisciplinary / Responsible AI / Science / Science (multidisciplinary)