Beyond Binary: Adaptive Frameworks for Autonomous AI Governance

Abstract

Since independent AI agents are becoming more and more common in important areas, governance models that go beyond traditional autonomy-control theories are needed. This study introduces and tests two new ideas for context-aware AI governance: the Adaptive Containment Framework (ACF) and the Weighted Autonomy Acceptance Index (WAAI). The ACF is a dynamic governance model that allows real-time autonomy calibration through ethical sensors and multi-stakeholder validation. The WAAI is a psychometrically robust metric (α = 0.89, CR = 0.91) for quantifying sector-specific autonomy thresholds. The study uses a sequential exploratory mixed-methods approach that includes Delphi studies with 15 subject experts, sector-stratified polls, and computer models to arrive at three key conclusions: (1) acceptance of autonomy is influenced by decision reversibility and harm potential, which explains 68% of cross-domain variation; (2) system explainability shows diminishing returns beyond an 82.3% comprehensibility threshold (χ²(3) = 24.71, p < 0.001), ending long-running XAI debates; and (3) uncertainty avoidance is the most important cultural factor explaining 41.3% of cross-national variation in acceptance of autonomy (Sobel’s z = 3.28, p < 0.001). The ACF performs better than static frameworks in many ways. It lowers bias events by 41% while keeping 92% of autonomy’s efficiency benefits and going above and beyond static frameworks in operating freedom by 119%. The way modern society think about dynamic equilibrium government has changed since these changes were made. They give politicians authority rules that are specific to a sector and developers ways to carry out projects that are sensitive to different cultures.

Received: 12 July 2025 | Revised: 10 November 2025 | Accepted: 27 November 2025

Conflict of Interest

The author declares that he has no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Gabriel Silva-Atencio: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.