You don't need to see an AI's secret code to know if it's safe—you just need to read its chat history.
While policy-makers argue we need 'open weights' or proprietary code access to ensure AI safety, this paper demonstrates that the most consequential harms can be detected purely from turn-by-turn dialogue records. This challenges the massive global push for model transparency by showing that the 'screen' is more important for governance than the 'hood.'
<p><span>The Light at the Door: MAP and the Interaction-Visible Governance of the Black Box<b></b></span></p>
SSRN · 6316458
The dominant assumption in AI governance is that meaningful auditing requires access to model internals. This paper argues that assumption is wrong in a way that has significant consequences for every governance framework currently in operation. The most consequential AI harms are not located inside the model. They are located in the interaction record: the visible, turn-by-turn exchange between system and user. The Meaning Audit Protocol (MAP) operationalises this claim. Applied turn by turn to