Multi-Agent LLMs as Ethics Advocates for AI-Based Systems
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Incorporating ethics into the requirement elicitation process is essential for creating ethically aligned systems. Although eliciting manual ethics requirements is effective, it requires diverse input from multiple stakeholders, which can be challenging due to time and resource constraints. Moreover, it is often given a low priority in the requirements elicitation process. This study proposes a framework for generating ethics requirements drafts by introducing an ethics advocate agent in a multi-agent LLM setting. This agent critiques and provides input on ethical issues based on the system description. The proposed framework is evaluated through two case studies from different contexts, demonstrating that it captures the majority of ethics requirements identified by researchers during 30-minute interviews and introduces several additional relevant requirements. However, it also highlights reliability issues in generating ethics requirements, emphasizing the need for human feedback in this sensitive domain. We believe this work can facilitate the broader adoption of ethics in the requirements engineering process, ultimately leading to more ethically aligned products.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 37 references that are in the map. First posted on arXiv 11 Jul 2025.
Builds on
14 of 37 references.
Closest ancestors · 3
Same direction, newest first
- 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future5 Aug
- 2024MARE: Multi-Agents Collaboration Framework for Requirements Engineering6 May
- 2024LLM-based agents for automating the enhancement of user story quality: An early report14 Mar
Other references · 11
- 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 769
- 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
- 2022How Different Groups Prioritize Ethical Values for Responsible AIcited by 49
- 2023Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMscited by 42
- 2023ChatGPT as a tool for User Story Quality Evaluation: Trustworthy Out of the Box?cited by 13
- 2023Requirements Engineering using Generative AI: Prompts and Prompting Patternscited by 13
- 2023RAI Guidelines: Method for Generating Responsible AI Guidelines Grounded in Regulations and Usable by (Non-)Technical Rolescited by 9
- 2023Requirements Engineering Framework for Human-centered Artificial Intelligence Software Systemscited by 7
- 2024Normative Requirements Operationalization with Large Language Modelscited by 5
- 2025Leveraging LLMs for User Stories in AI Systems: UStAI Datasetcited by 4
- 2023Requirements Elicitation and Modelling of Artificial Intelligence Systems: An Empirical Studycited by 2
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Citing papers per year: 2025: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
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