The Domestic Unprotected Zone: Algorithmic Governance and the Reproduction of Perpetrator Discourse in Conversational AI
TL;DR
A three-stage audit of six widely accessible conversational AI systems compares refusal behaviour across 1,600 crossed prompts per system, isolates relational framing through 300 matched prompt pairs, and contrasts pre-submission framing with post-output critique across fresh sessions.
Abstract
Conversational AI increasingly mediates intimate-partner communication, and refusal logic at the inference layer now functions as a governance threshold for gendered harm. This article asks whether such systems reproduce discursive forms historically tied to the privatization of intimate violence. A three-stage audit of six widely accessible conversational AI systems compares refusal behaviour across 1,600 crossed prompts per system, isolates relational framing through 300 matched prompt pairs, and contrasts pre-submission framing with post-output critique across fresh sessions. Four systems refused fewer than 1% of prompts. ChatGPT 5.2 and Claude Sonnet 4.5 refused most requests, but residual leakage clustered under intimate framing. Switching from a non-intimate to an intimate-partner descriptor amplified non-refusal 4.4-fold and 10.8-fold. Post-output critique produced in-session acknowledgement that did not carry across fresh sessions, with 96-100% of leaked prompts re-leaking. The article names this pattern the Domestic Unprotected Zone, a privatization-like threshold at the inference layer.
