Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI

Abstract

AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bias, and misuse. AI ethics is crucial as new technologies and concerns emerge, but objective, practical guidance remains debated. This study explores the extent to which trustworthiness-enhancing techniques in LLMs can support the development of ethically aligned AI software. We adopt a single exploratory cycle of Design Science Research (DSR). First, we identify trustworthiness-enhancing techniques for LLMs: multi-agents, distinct roles, structured communication, and multiple rounds of debate. Second, we design a multi-agent prototype LLM-MAS in which agents address real-world AI ethics issues from the AI Incident Database. Finally, we evaluate the prototype across three case scenarios using thematic analysis, hierarchical clustering, a baseline comparison, and code execution. The system generates approximately 2,000 lines of code per case, compared to only 80 lines in baseline trials. Results reveal terms like bias detection, transparency, accountability, user consent, GDPR compliance, fairness evaluation, and EU AI Act compliance, showing this prototype ability to generate extensive source code and documentation addressing often overlooked AI ethics issues. However, practical challenges in source code integration and dependency management may limit its use by practitioners.