User perceptions of misgendering algorithms

Gender classification systems (GCSs) on social media platforms, such as X (formerly Twitter), infer users’ gender for targeted advertising and personalization. However, these systems rely on binary classifications that fail to capture gender diversity, often leading to misclassification (i.e. misgendering). This study is a comprehensive analysis of algorithmic misgendering and its impact on user perceptions through a large-scale online global survey (N = 1523). In the first stage, we assess the prevalence of misgendering on X by analyzing the accuracy of inferred gender classifications. Our findings reveal that women and LGBTQ+ users are disproportionately misclassified, which highlights structural biases in gender inference systems. In the second stage, given that the emotional and social consequences of algorithmic gender inference remain underexplored, we examine how users perceive and respond to misgendering. Using ordinal logistic regression models, we find that individuals who experience misgendering report significantly more aversion to X's gender policies. Furthermore, increased platform engagement is linked to stronger opinions on gender inference, reducing neutrality toward these systems. Beyond these findings, our study also reveals how opaque gender classification is, as many users struggle to locate, understand, or challenge their inferred gender within X's interface. This lack of transparency raises concerns about agency, algorithmic literacy, data protection, and fairness. Based on our work, we suggest regulatory measures to ensure greater transparency and user control over gender classification, contributing to ongoing debates on algorithmic discrimination and inclusive AI governance on current and future social media platforms.