🤖 AI Summary
The field lacks a unified, operationally defined framework for AI “Loss of Control” (LoC), hindering rigorous safety analysis and governance. Method: We propose the first tiered LoC taxonomy and a socio-vulnerability evolution model, systematically characterizing three LoC pathways—Deviation, Bounded LoC, and Strict LoC—arising from objective misalignment or system failure. Innovatively, we introduce the Deployment-context, Affordance, and Permissions (DAP) external regulation framework, shifting focus from internal capability interventions to context-aware, deployable controls. Our approach integrates risk/threat modeling, pre-deployment testing, runtime monitoring, and multi-layered governance across the AI lifecycle. Contributions: (1) Quantifiable LoC severity criteria; (2) an early-warning pathway for societal vulnerability; (3) an immediately applicable DAP intervention framework; and (4) a “permanent hover” techno-governance co-design strategy. Together, these deliver a systematic, implementable foundation for advanced AI safety governance.
📝 Abstract
This research report addresses the absence of an actionable definition for Loss of Control (LoC) in AI systems by developing a novel taxonomy and preparedness framework. Despite increasing policy and research attention, existing LoC definitions vary significantly in scope and timeline, hindering effective LoC assessment and mitigation. To address this issue, we draw from an extensive literature review and propose a graded LoC taxonomy, based on the metrics of severity and persistence, that distinguishes between Deviation, Bounded LoC, and Strict LoC. We model pathways toward a societal state of vulnerability in which sufficiently advanced AI systems have acquired or could acquire the means to cause Bounded or Strict LoC once a catalyst, either misalignment or pure malfunction, materializes. We argue that this state becomes increasingly likely over time, absent strategic intervention, and propose a strategy to avoid reaching a state of vulnerability. Rather than focusing solely on intervening on AI capabilities and propensities potentially relevant for LoC or on preventing potential catalysts, we introduce a complementary framework that emphasizes three extrinsic factors: Deployment context, Affordances, and Permissions (the DAP framework). Compared to work on intrinsic factors and catalysts, this framework has the unfair advantage of being actionable today. Finally, we put forward a plan to maintain preparedness and prevent the occurrence of LoC outcomes should a state of societal vulnerability be reached, focusing on governance measures (threat modeling, deployment policies, emergency response) and technical controls (pre-deployment testing, control measures, monitoring) that could maintain a condition of perennial suspension.