arXiv:2606.12442v1 Announce Type: cross Abstract: At present, loss of control risks have gained much prominence in public discussion, particularly in relation to AI, with extensive discourse present among academics, frontier labs, and even governments. However, in the existing literature, the concept seems to rest on surprisingly weak foundations, where even those that discuss loss of control extensively do not first establish what control is and what exactly is being lost. Our paper aims to address these gaps. We establish a working definition of control by anchoring it to the "setting and ge
The paper is published as public discourse, academic research, and government attention on AI loss of control reaches critical mass, highlighting a current gap in foundational definitions.
A sophisticated understanding of AI control is crucial for effective policy-making, responsible development, and managing existential risks in an increasingly AI-driven world.
This research provides a more robust conceptual framework for 'AI loss of control,' shifting the discourse from vague concerns to more defined parameters for analysis and mitigation.
- · AI safety researchers
- · Policymakers
- · Regulators
- · Frontier AI labs
- · Vague AI risk discourse
- · Unprepared governance bodies
The paper provides a common language for discussing AI control failures.
This common language enables more targeted and effective technical and policy interventions for AI safety.
Clearer definitions of control could accelerate the development of verifiable safe AI systems, potentially influencing international governance standards.
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Read at arXiv cs.AI