How can I build trustworthy, useful multi-agent AI?
Human-AI Governance (HAIG): A Trust-Utility Approach
This paper introduces the Human-AI Governance (HAIG) framework, a new way of thinking about how we manage and regulate AI as it becomes more sophisticated, especially in multi-agent systems. Instead of sorting AI systems into fixed categories, HAIG looks at how human-AI relationships change gradually along three scales: who has decision-making power, how much the AI can operate on its own, and who is responsible when things go wrong.
Key points for LLM-based multi-agent systems include: Emergent capabilities of LLMs require flexible governance that adapts to changing AI behavior instead of relying on fixed rules. The ability of LLMs to reason and explain their decisions becomes crucial for establishing trust, especially as they take on more decision-making authority. Context matters – the same LLM can require different governance approaches depending on how it's used. As multi-agent systems become more common, we need to shift from trusting individual AIs to trusting how they interact as a group. "Trust dynamics," or how trust is built and maintained, should be central to how we govern AI. HAIG emphasizes making AI useful while minimizing harm, with stronger oversight for AI systems with greater authority.