study
How should governments prepare for an AI loss-of-control crisis?
No one can reliably predict when, where, or how an AI loss-of-control crisis might happen. Governments must be ready to respond. What capabilities will they need? And which are most important?
Join our expert panel | Time commitment <2 hours in total | Respond to consultations in November and January | Help validate a method for advanced AI risk planning | Register your interest by 16th of November 2026.
Who we are looking for
People whose work or research has given them a well-grounded understanding of either:
how advanced AI could go wrong, and how it is governed or evaluated (e.g. through work in AI policy, safety, evaluation or assurance, or related research); or
how governments respond to crises, and what they can and cannot do (e.g. through work in or close to government, public administration, regulation, or emergency or security planning).
Please apply, even if you are unsure whether you qualify: we will check eligibility for everyone.
What we ask
Participate in two consultation rounds in November and January (dates to be confirmed with at least two weeks’ notice).
Use your expertise to assess the effectiveness of government capabilities in different AI loss-of-control scenarios.
Complete in your own time: each round is open for about two weeks, and takes 20–60 minutes to complete.
Expert panel members are anonymous by default; experts who complete both rounds may choose to be credited by name in the paper.
The study is unpaid and voluntary. Privacy notice.
The study
The Arcadia Impact AI Governance Taskforce is running a study on the capabilities that governments will need for AI loss-of-control crises. By combining the judgment of many experts using Delphi methodology, the study aims to build a credible evidence base in the absence of empirical data or existing practice for new AI risks.
Why this matters
Rapid development and sudden capability jumps [1] make advanced AI hard to predict [2,3]. When the future is too uncertain to forecast, “predict-then-act” is not fit for purpose. Instead, effective risk mitigation shifts from asking which risks are most likely to asking which government capabilities protect against most risks [4].
This is an application of “decision making under deep uncertainty” (DMDU), an approach to planning that is well established in fields such as defence. Frontier AI developers’ “if-then” commitments partly follow this logic [5], but governments have yet to apply it to AI risks.
Building on our pilot
The study will build on our previous project that piloted a lightweight method for applying “decision making under deep uncertainty” principles to advanced AI risk planning. The pilot rated illustrative government capabilities against AI-crisis scenarios [6]. The results revealed a “no-regret” core of capabilities useful across many scenarios, plus “insurance” for the most severe.
Our new study will test whether a panel of independent experts, applying the same method, converges on a clear prioritisation of the capabilities governments will need to respond to an AI loss-of-control crisis. We aim to validate the method as a tool to help governments plan and prepare for unpredictable AI crises.
Join our expert panel
Registering takes about two minutes. Know others who should take part? Please forward this page.
References
1. Wei et al. (2022), Emergent abilities of large language models.
2. Weidinger et al. (2023), Sociotechnical safety evaluation of generative AI systems.
3. Hammond et al. (2025), Multi-agent risks from advanced AI.
4. Davis (2002), Capabilities-based planning, RAND.
5. METR (2025), Common elements of frontier AI safety policies.
6. Arcadia Impact AI Governance Taskforce (2026), Capability planning & preparedness.
Frequently Asked Questions
Who is this for?
People with either kind of understanding described above. You do not need both.
What happens in a Delphi study?
Our Delphi study will ask a panel of experts for independent, anonymous judgements over two rounds. In round one, you rate a set of government capabilities against one or more loss-of-control scenarios, giving your reasoning in each case. In round two, you see anonymised results and reasoning from other members of the expert panel and can choose to revise any of your round one ratings. Sharing ratings and reasoning lets the panel pool its expertise, while anonymity stops any one voice dominating. For more on the Delphi method: RAND’s guidance on Delphi panels.
How long does it take?
Each round is open for one to two weeks, time-dependent, with a typical ask of 20–60 minutes per round, depending on how many capabilities you choose to rate.
When will we know the round dates?
Round one is expected in mid-late November, round two is expected in January. Provisional dates will be confirmed to registered experts in due course and any changes made with at least two weeks’ notice.
Is it paid?
The study is unpaid and voluntary.
Will I be named?
Not unless you choose to be. Ratings are anonymous by default. Experts who complete both rounds may choose to be credited by name in the paper.
What happens to my data?
See the privacy notice.
What happens next?
We check everyone’s eligibility, then contact eligible experts to invite them to a short onboarding call.
Can I suggest others?
Yes! Please forward this page.