Misgendering algorithms: Insights from a cross-sectional survey on algorithmic gender classification in social media

Social media platforms rely on Gender Classification Systems (GCSs) to infer users’ gender from behavioral and demographic data, often without explicit consent. These systems optimize targeted advertising and user engagement but introduce significant ethical and regulatory concerns related to algorithmic bias, privacy, data governance, and accountability. Our study presents a large-scale survey (N=1642) analyzing the accuracy and implications of X’s (formerly Twitter) gender inference mechanisms that reveal systemic biases that disproportionately impact marginalized communities. Our findings indicate that men are less likely to experience misclassification than women. Furthermore, LGBTIQ+ individuals and those with non-conforming gender expressions face significantly higher risks of algorithmic misidentification. These results expose critical vulnerabilities in automated profiling systems and highlight the limitations of reductionist, binary technical frameworks applied to the inherently complex and fluid nature of gender identity. Our work underscores the urgent need for improved information management practices involving GCSs, emphasizing compliance, transparency, and user agency. By addressing these challenges, platforms can better align with evolving regulatory frameworks and societal expectations regarding data responsibility, fairness, and inclusion. These insights contribute to the growing imperative for inclusive, rights-based algorithmic governance across social media platforms.