Invisible Biases, Visible Harm: Algorithmic Misogyny and Gendered Vulnerabilities in India

Contemporary feminists critique digital governance, emphasising the widespread problem of algorithmic misogyny. This paper demonstrates how automated institutions operate within some vital spheres, reiterating and generating gendered injustices whose implications are deeply rooted in social policy, welfare, and civic equality in India and beyond. Data bias, unclear decision-making, platform amplification, and labour algorithms contribute to harm, as seen in welfare exclusion linked to Aadhaar and the online auctioning of women on Sulli Deals and Bulli Bai apps. The COVID-19 pandemic intensified these vulnerabilities. Mechanisms and effects are traced through the integration of feminist theory, critical algorithm studies, policy analysis, and empirical evidence. This paper aims to align the Beijing+30 renewal with gender audits and feminist governance by analysing “algorithmic misogyny,” uncovering patterns of exclusion and violence, and proposing policy pathways to hold algorithmic systems accountable to gender justice.