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arXiv · 2606.12442

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

Ze Shen Chin·Maurice Chiodo·Dennis Müller·Coleman Snell
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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 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.

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Ze Shen Chin, Maurice Chiodo, Dennis Müller, Coleman Snell. 2026-07-21. Reframing AI Loss of Control: What Control Is, How to Have It, How to Lose It. https://arxiv.org/abs/2606.12442

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