Human-AI Governance (HAIG): A Trust-Utility Approach (2505.01651v2)

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.

• The paper introduces the Human-AI Governance (HAIG) framework, a trust-utility approach using dimensions, continua, and thresholds to manage evolving human-AI relationships.
• HAIG operates across three key dimensions: Decision Authority Distribution, Process Autonomy, and Accountability Configuration, allowing for granular examination of agency and trust shifts.
• The framework offers practical insights for navigating governance challenges and calibrating trust in complex scenarios, demonstrated through applications in healthcare and complementing regulatory efforts like the EU AI Act.

Human-AI Governance: Insights from the HAIG Framework

Zeynep Engin's paper presents the Human-AI Governance (HAIG) framework, a nuanced approach to understanding and managing trust dynamics in human-AI relationships. As AI systems evolve from tools to partners with increasingly complex interdependencies, the need for adaptive governance models becomes apparent. The HAIG framework introduces a trust-utility oriented method comprising dimensions, continua, and thresholds that provide both theoretical and practical insights.

Key Features of the HAIG Framework

The HAIG framework operates on three primary dimensions: Decision Authority Distribution, Process Autonomy, and Accountability Configuration. These dimensions enable a granular examination of how agency and trust evolve in AI systems. Unlike categorical governance models, HAIG emphasizes continuous shifts in agency, capturing the fluidity of human-AI interactions in real-world applications.

1. Decision Authority Distribution involves the allocation of decision power between humans and AI, ranging from full human control to AI-dominant structures. This dimension is critical in sectors like healthcare, where diagnostic processes exemplify bi-directional trust dynamics.
2. Process Autonomy refers to the extent of AI operation without human intervention, challenging oversight mechanisms and demanding novel boundary detection strategies. This evolution is evident in autonomous driving, where process autonomy fluctuates based on environmental conditions.
3. Accountability Configuration tackles the distribution of responsibility across human and AI agents, highlighting emergent capabilities that defy static accountability models. This is pertinent in areas such as algorithmic governance, where AI systems autonomously adapt and optimize their operations.

Governance Challenges and Trust Evolution

Engin identifies significant governance challenges in accommodating contextual variation and technological evolution, illustrating the limits of existing categorical models. The HAIG framework addresses these challenges through its dimensional approach, helping to manage and calibrate trust across complex scenarios.

Critical trust thresholds marked within the HAIG framework denote pivotal points of governance adaptation. For instance, the "Verification to Delegation Threshold" signifies transitions from comprehensive human verification to statistical sampling. These thresholds highlight profound shifts in governance requirements and guide organizations in leveraging trust-building strategies tailored to their specific HAIG continua positioning.

Practical Applications and Implications

Through case studies in healthcare and EU regulation, Engin demonstrates the practical utility of the HAIG framework. In healthcare, the framework's application elucidates the evolving trust dynamics between AI systems and clinicians, necessitating corresponding adjustments in decision authority and accountability. With AI systems collaboratively navigating these changes, HAIG facilitates balanced oversight that enhances clinical autonomy without compromising patient safety.

The HAIG framework complements the EU AI Act's risk-based approach by offering a flexible, trust-oriented governance scaffold. It addresses categorical limitations through precise calibration along HAIG dimensions, supporting anticipatory regulation that accommodates rapid technological shifts and contextual deployments. This adaptability is crucial for sectors confronting complex, multi-agent interactions, such as smart city infrastructure.

Conclusion and Future Directions

The HAIG framework provides a robust approach to managing the continuous evolution of human-AI relationships, offering insights that extend beyond traditional governance models. By positioning trust dynamics at its core, HAIG fosters governance strategies that optimize AI utility while ensuring safeguards are in place. As jurisdictions explore regulatory frameworks that align with agile AI developments, HAIG presents a foundational paradigm for adaptive and context-sensitive AI governance.

Further empirical research is needed to validate HAIG across diverse domains, particularly in understanding the interplay between technological capabilities and trust requirements. As AI systems continue to integrate deeply into socio-economic structures, the HAIG framework serves not only as a complement but as a potential cornerstone for future AI governance models.