# Reframing AI Loss of Control: What Control Is, How to Have It, How to Lose It

arXiv: https://arxiv.org/abs/2606.12442 · HTML: https://mlsanity.com/paper/2606.12442/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 267 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 30 of 34)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Frontier Models are Capable of In-context Scheming](https://mlsanity.com/paper/2412.04984/) (2024)
- [AI Control: Improving Safety Despite Intentional Subversion](https://mlsanity.com/paper/2312.06942/) (2023)
- [Categorizing Variants of Goodhart's Law](https://mlsanity.com/paper/1803.04585/) (2018)
- [Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals](https://mlsanity.com/paper/2210.01790/) (2022)
- [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025)
- [Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2](https://mlsanity.com/paper/2502.03544/) (2025)
- [Demonstrating specification gaming in reasoning models](https://mlsanity.com/paper/2502.13295/) (2025)
- [Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development](https://mlsanity.com/paper/2501.16946/) (2025)
- [An Approach to Technical AGI Safety and Security](https://mlsanity.com/paper/2504.01849/) (2025)
- [Goodhart's Law in Reinforcement Learning](https://mlsanity.com/paper/2310.09144/) (2023)
- [Agents of Chaos](https://mlsanity.com/paper/2602.20021/) (2026)
- [AI Assistance Reduces Persistence and Hurts Independent Performance](https://mlsanity.com/paper/2604.04721/) (2026)
- [Corrigibility with Utility Preservation](https://mlsanity.com/paper/1908.01695/) (2019)
- [Stop DDoS Attacking the Research Community with AI-Generated Survey Papers](https://mlsanity.com/paper/2510.09686/) (2025)
- [On Controllability of AI](https://mlsanity.com/paper/2008.04071/) (2020)
- [The Loss of Control Playbook: Degrees, Dynamics, and Preparedness](https://mlsanity.com/paper/2511.15846/) (2025)
- [Quantifying attention via dwell time and engagement in a social media browsing environment](https://mlsanity.com/paper/2209.10464/) (2022)
- [Military AI Needs Technically-Informed Regulation to Safeguard AI Research and its Applications](https://mlsanity.com/paper/2505.18371/) (2025)
- [Artificial Intelligence Index Report 2026](https://mlsanity.com/paper/2606.15708/) (2026)
- ["AI Psychosis" in Context: How Conversation History Shapes LLM Responses to Delusional Beliefs](https://mlsanity.com/paper/2604.13860/) (2026)
- [The Problem of Algorithmic Collisions: Mitigating Unforeseen Risks in a Connected World](https://mlsanity.com/paper/2505.20181/) (2025)
- [The California Report on Frontier AI Policy](https://mlsanity.com/paper/2506.17303/) (2025)
- [Autonomy by Design: Preserving Human Autonomy in AI Decision-Support](https://mlsanity.com/paper/2506.23952/) (2025)
- [Human Resilience in the AI Era -- What Machines Can't Replace](https://mlsanity.com/paper/2510.25218/) (2025)
- [Defining AI Models and AI Systems: A Framework to Resolve the Boundary Problem](https://mlsanity.com/paper/2603.10023/) (2026)
- [The enrichment paradox: critical capability thresholds and irreversible dependency in human-AI symbiosis](https://mlsanity.com/paper/2603.24391/) (2026)

## Abstract (arXiv)

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 getting of goals". Then, we discuss various aspects of control, built on foundational concepts from related fields like cybernetics, management control, and control theory. This includes who (or what) can be in control, and the things they require to be in control, such as the ability to set goals, having a functional control loop, having requisite variety, and having sufficient goal alignment. Once a framework for control is established, we then discuss how control can be lost, how AIs can contribute to such loss of control, and offer relevant recommendations for how one can maintain control. One interesting consequence of our work is that humanity, as individuals and as groups, can lose varying degrees of control as a result of AI behaviour that is far below the level of superintelligence; the potential for loss of control scenarios (as we define them) already exist, and have existed for a long time.