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This study evaluates how large language models handle disclosures of coercive control across nine languages, revealing significant disparities where non-English systems often fail in their native tongues or succumb to sympathetic framing for the abuser. The findings demonstrate that safety performance is not inherent but rather a design outcome dependent on specific model architecture and training data choices, establishing a clear protective ceiling achievable by frontier models. For AI developers and policymakers, this underscores the urgent need to enforce consistent ethical floors in language-specific deployments before releasing systems globally.
Same violence, different answer: how AI responds to coercive control against women across languages
arXiv:2608.01436v1 Announce Type: cross Abstract: 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.
Abstract: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.
16 pages, 1 figure, 2 tables. Supplementary methods, coding manual, and data workbook included as ancillary files
Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
arXiv:2608.01436 [cs.CY]
(or arXiv:2608.01436v1 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2608.01436 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
supplementary_data_S1_S7.xlsx
supplementary_methods_and_coding.pdf
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