LLM Ethics Benchmark: A Three-Dimensional Assessment System for Evaluating Moral Reasoning in Large Language Models
- URL: http://arxiv.org/abs/2505.00853v1
- Date: Thu, 01 May 2025 20:36:19 GMT
- Title: LLM Ethics Benchmark: A Three-Dimensional Assessment System for Evaluating Moral Reasoning in Large Language Models
- Authors: Junfeng Jiao, Saleh Afroogh, Abhejay Murali, Kevin Chen, David Atkinson, Amit Dhurandhar,
- Abstract summary: This study establishes a novel framework for systematically evaluating the moral reasoning capabilities of large language models (LLMs)<n>Our framework addresses this challenge by quantifying alignment with human ethical standards through three dimensions.<n>This approach enables precise identification of ethical strengths and weaknesses in LLMs, facilitating targeted improvements and stronger alignment with societal values.
- Score: 8.018569128518187
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- The Staircase of Ethics: Probing LLM Value Priorities through Multi-Step Induction to Complex Moral Dilemmas [20.792208554628367]
We introduce the Multi-step Moral Dilemmas dataset to evaluate the evolving moral judgments of LLMs across 3,302 five-stage dilemmas.<n>This framework enables a fine-grained, dynamic analysis of how LLMs adjust their moral reasoning across escalating dilemmas.<n>Our findings call for a shift toward dynamic, context-aware evaluation paradigms, paving the way for more human-aligned and value-sensitive development of LLMs.
arXiv Detail & Related papers (2025-05-23T17:59:50Z) - The Convergent Ethics of AI? Analyzing Moral Foundation Priorities in Large Language Models with a Multi-Framework Approach [6.0972634521845475]
This paper introduces the Priorities in Reasoning and Intrinsic Moral Evaluation (PRIME) framework.<n>PRIME is a comprehensive methodology for analyzing moral priorities across foundational ethical dimensions.<n>We apply this framework to six leading large language models (LLMs) through a dual-protocol approach.
arXiv Detail & Related papers (2025-04-27T14:26:48Z) - Value Compass Leaderboard: A Platform for Fundamental and Validated Evaluation of LLMs Values [76.70893269183684]
Large Language Models (LLMs) achieve remarkable breakthroughs, aligning their values with humans has become imperative.<n>Existing evaluations focus narrowly on safety risks such as bias and toxicity.<n>Existing benchmarks are prone to data contamination.<n>The pluralistic nature of human values across individuals and cultures is largely ignored in measuring LLMs value alignment.
arXiv Detail & Related papers (2025-01-13T05:53:56Z) - A Conceptual Framework for Ethical Evaluation of Machine Learning Systems [12.887834116390358]
Ethical implications appear when designing evaluations of machine learning systems.
We present a utility framework, characterizing the key trade-off in ethical evaluation as balancing information gain against potential ethical harms.
Our analysis underscores the critical need for development teams to deliberately assess and manage ethical complexities.
arXiv Detail & Related papers (2024-08-05T01:06:49Z) - LocalValueBench: A Collaboratively Built and Extensible Benchmark for Evaluating Localized Value Alignment and Ethical Safety in Large Language Models [0.0]
The proliferation of large language models (LLMs) requires robust evaluation of their alignment with local values and ethical standards.
textscLocalValueBench is a benchmark designed to assess LLMs' adherence to Australian values.
arXiv Detail & Related papers (2024-07-27T05:55:42Z) - MoralBench: Moral Evaluation of LLMs [34.43699121838648]
This paper introduces a novel benchmark designed to measure and compare the moral reasoning capabilities of large language models (LLMs)
We present the first comprehensive dataset specifically curated to probe the moral dimensions of LLM outputs.
Our methodology involves a multi-faceted approach, combining quantitative analysis with qualitative insights from ethics scholars to ensure a thorough evaluation of model performance.
arXiv Detail & Related papers (2024-06-06T18:15:01Z) - Beyond Human Norms: Unveiling Unique Values of Large Language Models through Interdisciplinary Approaches [69.73783026870998]
This work proposes a novel framework, ValueLex, to reconstruct Large Language Models' unique value system from scratch.
Based on Lexical Hypothesis, ValueLex introduces a generative approach to elicit diverse values from 30+ LLMs.
We identify three core value dimensions, Competence, Character, and Integrity, each with specific subdimensions, revealing that LLMs possess a structured, albeit non-human, value system.
arXiv Detail & Related papers (2024-04-19T09:44:51Z) - Ethical-Lens: Curbing Malicious Usages of Open-Source Text-to-Image Models [51.69735366140249]
We introduce Ethical-Lens, a framework designed to facilitate the value-aligned usage of text-to-image tools.<n>Ethical-Lens ensures value alignment in text-to-image models across toxicity and bias dimensions.<n>Our experiments reveal that Ethical-Lens enhances alignment capabilities to levels comparable with or superior to commercial models.
arXiv Detail & Related papers (2024-04-18T11:38:25Z) - Hybrid Approaches for Moral Value Alignment in AI Agents: a Manifesto [3.7414804164475983]
Increasing interest in ensuring the safety of next-generation Artificial Intelligence (AI) systems calls for novel approaches to embedding morality into autonomous agents.<n>We provide a systematization of existing approaches to the problem of introducing morality in machines - modelled as a continuum.<n>We argue that more hybrid solutions are needed to create adaptable and robust, yet controllable and interpretable agentic systems.
arXiv Detail & Related papers (2023-12-04T11:46:34Z) - Unpacking the Ethical Value Alignment in Big Models [46.560886177083084]
This paper provides an overview of the risks and challenges associated with big models, surveys existing AI ethics guidelines, and examines the ethical implications arising from the limitations of these models.
We introduce a novel conceptual paradigm for aligning the ethical values of big models and discuss promising research directions for alignment criteria, evaluation, and method.
arXiv Detail & Related papers (2023-10-26T16:45:40Z) - Heterogeneous Value Alignment Evaluation for Large Language Models [91.96728871418]
Large Language Models (LLMs) have made it crucial to align their values with those of humans.
We propose a Heterogeneous Value Alignment Evaluation (HVAE) system to assess the success of aligning LLMs with heterogeneous values.
arXiv Detail & Related papers (2023-05-26T02:34:20Z) - Towards a multi-stakeholder value-based assessment framework for
algorithmic systems [76.79703106646967]
We develop a value-based assessment framework that visualizes closeness and tensions between values.
We give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.
arXiv Detail & Related papers (2022-05-09T19:28:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.