AI Politician

David Abecassis, Felix Michalak, Nguyen Dang Nhat Anh, Zhiyi Xu

This project explores the potential for AI chatbots to enhance participative democracy by allowing a politician to engage with a large number of constituents in personalized conversations at scale. By creating a chatbot that emulates a specific politician and is knowledgeable about a key policy issue, we aim to demonstrate how AI could be used to promote civic engagement and democratic participation.

Reviewer's Comments

Reviewer's Comments

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Nina Rimsky

Chatbot worked well, good answers - can see something like this being useful

Konrad Seifert

I am intrigued. I didn’t expect to see disenfranchisement as a threat here before but agree it is in general (not exactly from AI, however). This will also be useful in keeping representatives accountable. Bottleneck will be the data collection and security to avoid interference/ensure verification to get politicians on board.

Jason Hoelscher-Obermaier

Really interesting idea and worth exploring more. It seems very plausible that AI simulacra of politicians will be deployed. Therefore, understanding how these systems would affect democratic processes and citizen’s attitudes seems really worthwhile. A useful addition would be to go into more detail on how such systems might itself come under adversarial attack and what the implications of that could be.

Andrey Anurin

An impressive demo! The bot seemed knowledgeable and steerable, and I expect we will see real systems like this deployed soon. However, I think the question of how a system like this could be used in the real world and impact the democratic process deserves more exploration, with potential usecases and impacts (in whichever direction) remaining vague.

Cite this work

@misc {

title={

AI Politician

},

author={

David Abecassis, Felix Michalak, Nguyen Dang Nhat Anh, Zhiyi Xu

},

date={

5/5/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

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We argue that epidemic-style monitoring and tools grounded in interpretable, physics-aligned dynamics can serve as early warning systems for cascading AI agentic failures.

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In this work, we adapt classical epidemiological modeling (specifically SEIR compartment models) to model adversarial behavior propagation in AI agents.

By solving systems of ODEs describing the systems with physics-informed neural networks (PINNs), we analyze stable and unstable equilibria, bifurcation points, and the effectiveness of interventions.

We estimate parameters from real-world data (e.g., adversarial success rates, detection latency, patching delays) and simulate attack propagation scenarios across 8 sectors (enterprise, retail, trading, development, customer service, academia, medical, and critical infrastructure AI tools).

Our results demonstrate how agent population dynamics interact with architectural and policy design interventions to stabilize the system.

This framework bridges concepts from dynamical systems and cybersecurity to offer a proactive, quantitative toolbox on AI safety.

We argue that epidemic-style monitoring and tools grounded in interpretable, physics-aligned dynamics can serve as early warning systems for cascading AI agentic failures.

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.