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Sprint projectJul 1, 2024

Detecting Deception with AI Tics 😉

Samuel Svenningsen, Ilan Moscovitz, Nikhil Kotecha · Team Polygraph

Submitted to Deception Detection Hackathon: Preventing AI deception. Sprint projects are early-stage work by participants, not Apart Research publications.

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We present a novel approach: intentionally inducing subtle "tics" in AI responses as a marker for deceptive behavior. By adding a system prompt, we embed innocuous yet detectable patterns that manifest when the AI knowingly engages in deception.

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  1. This work tests whether models follow a system prompt that tells them to mark any lie with a winky face. The robustness of the method is not discussed (though fine-tuning it in is suggested), nor is it compared to existing deception detection methods (e.g. contrast-consistent search and variants, activation vectors, blackbox lie detection). It is suggested that future models would find it hard to not include such tells if fine-tuned, but this statement is not supported. In particular, deception is only a problem if fine-tuning models for helpfulness & harmlessness does not work, so in any deception scenario, simple at least some simple fine-tuning strategies have already broken. Future work could explore whether fine-tuning such tells are more robust than other types of fine-tuning aimed at preventing or detecting deception.

Cite this project

@misc{svenningsen2024detecting,
  title = {{Detecting Deception with AI Tics 😉}},
  author = {Samuel Svenningsen and Ilan Moscovitz and Nikhil Kotecha},
  year = {2024},
  month = jul,
  note = {Submitted to Deception Detection Hackathon: Preventing AI deception, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/detecting-deception-with-ai-tics}},
  url = {https://apartresearch.com/sprints/projects/detecting-deception-with-ai-tics}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026