Human-AI Governance (HAIG): A Trust-Utility Approach

2025-05-03

AI Summary

Existing static “human-in-the-loop” classification frameworks fail to capture the dynamic evolution of human-AI trust as AI transitions from tool to collaborative partner—particularly amid foundation model emergent capabilities and multi-agent autonomous goal-setting, where decision authority, autonomy, and accountability exhibit continuous, non-binary evolution. Method: We propose the Human-AI Trust Governance (HAIG) framework, introducing the first dual-objective governance paradigm balancing trust and utility. It establishes a three-dimensional continuous spectrum—decision authority allocation, process autonomy, and accountability configuration—anchored by empirically grounded critical thresholds. HAIG integrates cross-level conceptual modeling, continuity-aware quantification, and threshold-triggered adaptive responses. Results: Evaluated in healthcare and EU regulatory contexts, HAIG demonstrates superior complementarity and dynamic adaptability over traditional frameworks, enabling proactive governance alignment with emergent AI capabilities.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Mechanism Design

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper introduces the HAIG framework for analysing trust dynamics across evolving human-AI relationships. Current categorical frameworks (e.g.,"human-in-the-loop"models) inadequately capture how AI systems evolve from tools to partners, particularly as foundation models demonstrate emergent capabilities and multi-agent systems exhibit autonomous goal-setting behaviours. As systems advance, agency redistributes in complex patterns that are better represented as positions along continua rather than discrete categories, though progression may include both gradual shifts and significant step changes. The HAIG framework operates across three levels: dimensions (Decision Authority Distribution, Process Autonomy, and Accountability Configuration), continua (gradual shifts along each dimension), and thresholds (critical points requiring governance adaptation). Unlike risk-based or principle-based approaches, HAIG adopts a trust-utility orientation, focusing on maintaining appropriate trust relationships that maximise utility while ensuring sufficient safeguards. Our analysis reveals how technical advances in self-supervision, reasoning authority, and distributed decision-making drive non-uniform trust evolution across both contextual variation and technological advancement. Case studies in healthcare and European regulation demonstrate how HAIG complements existing frameworks while offering a foundation for alternative approaches that anticipate governance challenges before they emerge.

Problem

• Analyzing trust dynamics in evolving human-AI relationships
• Redefining agency distribution beyond categorical frameworks
• Balancing trust-utility with safeguards in AI governance

Innovation

• HAIG framework analyzes trust dynamics in human-AI relationships
• Uses continua and thresholds to represent agency redistribution
• Adopts trust-utility approach to maximize utility with safeguards