BiasShield: An AI Browser Extension Against Online Misogyny

Abstract

Online spaces frequently expose women to sexualised and objectifying content, with documented harms including body dissatisfaction, anxiety, and depression. Automated moderation algorithms compound this through gendered bias by disproportionately classifying benign images of women as sexualised. Deepfake technologies have intensified the harms, with the victims being predominantly women. To counter these developments, we present BiasShield, a browser extension that identifies, audits, and enables users to manage exposure to misogynistic and deepfake content. We report on the design of a multimodal classifier and evaluate its capacity to detect misogynistic content while reducing gender-based false positives. By making algorithmic bias visible and actionable through exposure analytics and protective measures- including optional blurring of offensive content—BiasShield turns content moderation on the web into informed, user-based control.