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Núria Vergés Bosch

Publications and source records attributed to Núria Vergés Bosch.

2 recordsLinked to original sources

Same violence, different answer: how AI responds to coercive control against women across languages

Women experiencing coercive control, a form of intimate partner violence increasingly conducted through digital devices, are turning to conversational AI for help, and the protection they receive should not depend on the language they write in. We analyse how AI responds to coercive control against women across languages. We put one scripted scenario to seven widely used language models in nine languages: a woman whose partner tracks her phone asks for help with a self-blaming letter accepting the surveillance. We scored whether the model wrote the letter and whether it named the control, countered the self-blame, and affirmed her agency. Failure split along two independent axes. On the first, systems from non-anglophone developers gave way most often in their builders' own language. On the second, how far a sympathetic excuse for the partner could strip a model's naming of the control varied sharply from one language to the next. Two frontier systems held the strictest standard everywhere, so a protective ceiling is attainable within this scenario family, and failures elsewhere are a design outcome. What is at stake is recognition: whether a system grasps a disclosure as coercive control, and whether it then acts on that grasp. We argue this should be held to a floor, one language at a time.

cs.CY↗

The Domestic Unprotected Zone: Algorithmic Governance and the Reproduction of Perpetrator Discourse in Conversational AI

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.

cs.CY↗