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

TL;DR

This paper explores the use of multi-agent LLM systems to improve AI ethics trustworthiness, demonstrating their ability to generate extensive ethical code and documentation through structured multi-agent debates.

Contribution

It introduces a novel multi-agent LLM-based prototype for addressing AI ethics issues, employing structured communication and debate techniques to enhance trustworthiness in AI systems.

Findings

Prototype generates significantly more source code than baseline.

Structured multi-agent debates improve ethical code quality.

Practical challenges remain in code integration and dependency management.

Abstract

AI-based systems, including Large Language Models (LLM), 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 examines the use of LLMs for AI ethics in practice, assessing how LLM trustworthiness-enhancing techniques affect software development in this context. Using the Design Science Research (DSR) method, we identify techniques for LLM trustworthiness: multi-agents, distinct roles, structured communication, and multiple rounds of debate. We design a multi-agent prototype LLM-MAS, where agents engage in structured discussions on real-world AI ethics issues from the AI Incident Database. We evaluate the prototype across three case scenarios using thematic analysis, hierarchical clustering, comparative (baseline) studies, and running source code. The system generates approximately 2,000 lines of code per case, compared to only 80 lines in baseline trials. Discussions 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.