Grounded Ethical AI: A Demonstrative Approach with RAG-Enhanced Agents - de Cerqueira, José Antonio Siqueira; Khan, Ayman Asad; Rousi, Rebekah; Xi, Nannan; Hamari, Juho; Kemell, Kai-Kristian; Abrahamsson, Pekka (RWTH Aachen, 2025)
Large Language Models (LLMs) have become central in various fields, yet their trustworthiness remains a pressing concern, especially in developing ethically aligned AI-based systems. This paper presents a demonstration of an LLM-based multi-agent system incorporating Retrieval-Augmented Generation (RAG) to support developers in creating AI systems that align with legal and ethical guidelines. Leveraging documents like the EU AI Act, AI HLEG guidelines, and ISO/IEC 42001:2024, the prototype utilizes multiple agents with specialized roles, structured conversations, and debate rounds to enhance both ethical rigor and trustworthiness. Initial evaluations on real-world AI incidents reveal that this system can produce AI solutions adhering to specific ethical requirements, though further refinements are needed for citation accuracy and practical application. This demonstration illustrates the potential of RAG-enhanced LLMs to operationalize AI ethics and regulatory compliance within the development process, highlighting future directions for achieving more reliable and ethically robust AI solutions.
Keywords: AI ethics; AI4SE; Large Language Models; Trustworthiness; fi=Viestintätieteet|en=Communication Studies|