Enhancing Explainability, Robustness, and Autonomy: A Comprehensive Approach in Trustworthy AI

Recent advancements in AI, especially generative AI (gAI), are accelerating industrial digitalisation, with the market projected to grow significantly by 2030. However, challenges such as the black-box nature of AI decisions, biased data, and AI-generated hallucinations continue to hinder industrial trust. AI also requires better adaptability to dynamic environments and stronger accountability mechanisms. To address these challenges, this paper proposed an adaptive gAI-based multi-agent framework that enables collaboration between human actors and multiple AI agents, i.e. ExplainAgent, AuditAgent, RobustAgent and AutoAgent tailored to mirror and provide specialised support for the various aspects of trustworthy AI. Each of the agents will be clearly defined and specialised through the customisation of multiple modules encompassing 1) Communication and Cooperation, 2) Ensure Trust and 3) Execute and Evaluate Decisions. The framework focuses on improving explainability, fairness, and robustness while fostering human-AI collaboration with the aim of advancing trustworthy AI methods, tools and best practices leveraging AI and related technologies.