Detecting Deception with AI Tics 😉

Samuel Svenningsen, Ilan Moscovitz, Nikhil Kotecha

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.

Reviewer's Comments

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Rudolf L

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 work

@misc {

title={

@misc {

},

author={

Samuel Svenningsen, Ilan Moscovitz, Nikhil Kotecha

},

date={

7/1/24

},

organization={Apart Research},

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

howpublished={https://apartresearch.com}

}

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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.