An Ethical eValuation Agent (EeVA): Results of a Proof-of-Concept Test on a Prototype Agentic-like Workflow to Assist Ethical Deliberations
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Ethical deliberation is often misunderstood as a search for single right or wrong answers, creating difficulties for non-ethically trained personnel who must address ethically laden challenges. We developed EeVA, an agentic-like LLM-based workflow designed to support comparative ethical reflection rather than deliver definitive ethical answers. EeVA was programmed in n8n using three interconnected workflows: starter, worker, and emitter. It evaluated uploaded use cases against 10 ethical frameworks through evaluator and synthesis prompts. Proof-of-concept testing used three published cases from urban mobility, peer-to-peer energy trading, and social-service resource allocation. Across all cases, EeVA produced consistently structured framework-specific evaluations and integrated syntheses. Outputs differentiated between frameworks, identified convergences and divergences, recommended modifications to increase alignment, and highlighted persistent ethical tensions. Syntheses were readable for non-specialists and shifted attention away from simplistic answers toward design conditions, safeguards, and areas where full cross-framework agreement was unlikely. The findings suggest that LLMs can be organised into usable workflows that preserve ethical plurality while helping bridge the communicative gap between ethicists and non-ethically trained personnel. EeVA's value lies not in replacing ethicists or resolving moral disagreement, but in scaffolding structured ethical deliberation. EeVA offers a promising proof of concept for supporting ethical reflection where access to ethics expertise is limited. Further work is needed on reproducibility, human evaluation, user testing, and efficiency before it can be considered a mature tool.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 7 of its 23 references that are in the map. First posted on arXiv 5 May 2026.
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References · 7
- 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
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- 2017A Voting-Based System for Ethical Decision Makingcited by 55
- 2024MoralBench: Moral Evaluation of LLMscited by 50
- 2025LLM Ethics Benchmark: A Three-Dimensional Assessment System for Evaluating Moral Reasoning in Large Language Modelscited by 20
- 2022An Ethical Trajectory Planning Algorithm for Autonomous Vehiclescited by 15
- 2024Some Issues in Predictive Ethics Modeling: An Annotated Contrast Set of "Moral Stories"cited by 1
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- 2024Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI
- 2026Operationalizing Ethics for AI Agents: How Developers Encode Values into Repository Context Files
- 2026ETHOS: Towards a Modular Ethics Framework for Clinical Multi-Agent Systems
- 2024Informed AI Regulation: Comparing the Ethical Frameworks of Leading LLM Chatbots Using an Ethics-Based Audit to Assess Moral Reasoning and Normative Values
- 2025Beyond Ethical Alignment: Evaluating LLMs as Artificial Moral Assistants