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Sprint projectFeb 2, 2026London, UK
3rd place

Political Intelligence for AI Safety: The AI Risk Attitudes Survey (AIRAS)

Nicholas Botti, Jamie Coombs, Anna Konovalenko · Team AIRAS

Submitted to The Technical AI Governance Challenge. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Political Intelligence for AI Safety: The AI Risk Attitudes Survey (AIRAS)

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The AI safety and governance community is making progress on defining red lines around existential risk from advanced AI systems, and building verification infrastructure to support this objective. However, this is only half the battle. Implementing these red lines requires unprecedented international coordination, and enforcing them requires credible commitments from national governments, all during a period of increased geopolitical tensions. We present a whitepaper that makes the case that: Implementation and enforcement of red lines is as much a political question as it is a technical one While studies exist showing general public support for AI regulation, the depth of domestic political support among major powers for costly international AI regulation and willingness to make real sacrifices to enforce these red lines is understudied Rigorous policy-credible surveys of public opinion in this area would both enhance the AI safety community’s efforts to prioritize scarce resources to the most effective action areas and provide advocates a valuable source of evidence to point to when lobbying national governments and international organizations And therefore proposes and develops the methodology for AIRAS.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. This project provides good infrastructure for testing public willingness to bear real costs for security from AI systems. Generic polling showing support for "AI regulation" helps policymakers understand the direction of public preferences but not their strength — you can consistently observe publics that support more spending in every individual department while simultaneously wishing to cut overall government spending, or who favour more immigration from every specific group presented to them while wanting lower immigration overall - presumably because frequently when asked the public considers whether something is first order good/bad without implications for the general budget constraint. The same dynamic could plausibly apply to AI governance: abstract support for "careful management" may not survive contact with specific costs, so verifying this is important to get a sense of the depth of support for safety measures.

    The survey methodology is well-designed to help find this, with comprehension gates, relative priority and a large number of possible costs the public could be concerned about, meaning it should be able to provide a reasonable estimate of the gap between raw support and "hard support" that survives cost framing is a vital indicator for long-run political durability. The political viability scorecard is well formatted and gives a clear map for future policy design. I would be very interested to see how the public's level of commitment to various specific interventions in this framework compares to data not accounting for this.

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  2. Cool project idea! Systematically mapping political viability of specific AI safety red lines would indeed be very valuable. The design principles (especially comprehension gating and tradeoff testing) and the white paper do look like a serious attempt that is ready to be looked at and red-teamed by experts in survey design. Also congrts on the dashboard; it does a great job illustrating what actionable output could look like.

    A great next step would be to do quick validation of the survey with a few real human respondents. Even a very small pilot (a few dozens of respondents) would dramatically strengthen this. Another good step would be to strengthen the analysis of related literature on risk perception and public opinion methodology. I'd be particularly interested in seeing a deeper investigation of the difference betwween stated preferences and actual behavior or even political mobilization.

    I hope you pursue this project further and that it will eventually yield some very useful data!

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Cite this project

@misc{botti2026political,
  title = {{Political Intelligence for AI Safety: The AI Risk Attitudes Survey (AIRAS)}},
  author = {Nicholas Botti and Jamie Coombs and Anna Konovalenko},
  year = {2026},
  month = feb,
  note = {Submitted to The Technical AI Governance Challenge, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/political-intelligence-for-ai-safety-the-ai-risk-attitudes-survey-airas-dlpz}},
  url = {https://apartresearch.com/sprints/projects/political-intelligence-for-ai-safety-the-ai-risk-attitudes-survey-airas-dlpz}
}

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