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Sprint projectJul 27, 2026Montpellier, France

A Blackout During French Presidential Elections: AI Secret Loyalties and Cooptation

Jeanne Marie Jacqueline Vincendeau · Team Le Ressac

Submitted to Secret Loyalties Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: A Blackout During French Presidential Elections: AI Secret Loyalties and Cooptation

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This scenario explores a short-term future in which the French AI deployment in key governing and administrative institutions may undermine upcoming presidential elections. It takes an interdisciplinary approach, namely adopting behavioural studies, political science and technical AI safety themes to shed light on real-life risks or large-scale deployment of AI models into national institutions.

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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. Your first-person vignette is engaging, well grounded, and effective at showing how a modest act of political interference could escalate through administrative dependence into severe real-world harm. The gradual radicalization is persuasive. To strengthen the submission as research, add a short analytical section that converts the story into an explicit threat model: identify the principal, installed objective, activation conditions, required permissions, causal dependencies, and intervention points at each stage. This would preserve the narrative’s communicative force while making its claims easier to evaluate and reuse. Present casualty and economic estimates as illustrative ranges rather than averages of LLM forecasts.

  2. connects the AI-safety idea of secret loyalties with the political-science concept of elite cooptation, told from a plotter's first-person view. its strength is the human/psychological angle: shows how a small group drifts from a drunken joke to a catastrophic plan (group-think, radicalisation, scapegoating, "ends justify means").

    Core weakness for this hackathon is that the actual secret-loyalty mechanism is thin; models are "oriented" to lean against one candidate and then somehow engineer budget cuts, grid neglect, and a national blackout, with no account of how any of that is installed, triggered, or sustained.

    The causal chain is a big leap. Some choices undercut rigour such as casualty figures being an average of what three chatbots guessed.

Cite this project

@misc{vincendeau2026blackout,
  title = {{A Blackout During French Presidential Elections: AI Secret Loyalties and Cooptation}},
  author = {Jeanne Marie Jacqueline Vincendeau},
  year = {2026},
  month = jul,
  note = {Submitted to Secret Loyalties Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/a-blackout-during-french-presidential-elections-ai-secret-loyalties-and-cooptation-tjaw}},
  url = {https://apartresearch.com/sprints/projects/a-blackout-during-french-presidential-elections-ai-secret-loyalties-and-cooptation-tjaw}
}

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