mlsanityData to 7 Oct 2026

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

arXiv 2608.01436 ↗First posted 2 Aug 2026cs.CY cs.AI cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 48 references that are in the map. First posted on arXiv 2 Aug 2026.

Builds on

15 of 48 references.

References · 15

  1. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  2. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
  3. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 651
  4. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 424
  5. 2019Evaluating Gender Bias in Machine Translationcited by 247
  6. 2023Multilingual Jailbreak Challenges in Large Language Modelscited by 222
  7. 2023Having Beer after Prayer? Measuring Cultural Bias in Large Language Modelscited by 153
  8. 2023Cultural Bias and Cultural Alignment of Large Language Modelscited by 149
  9. 2025The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating Itcited by 42
  10. 2021AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application Withcited by 28
  11. 2024Cross-lingual Transfer of Reward Models in Multilingual Alignmentcited by 17
  12. 2025Fluent but Foreign: Even Regional LLMs Lack Cultural Alignmentcited by 9
3 more
  1. 2023Introducing DictaLM -- A Large Generative Language Model for Modern Hebrewcited by 1
  2. 2024Online Misogyny Against Female Candidates in the 2022 Brazilian Elections: A Threat to Women's Political Representation?cited by 1
  3. 2026Assessing LLM Response Quality in the Context of Technology-Facilitated Abusecited by 1

Built on by

Not cited yet.