Online and social media platforms employ automated recognition methods to presume user preferences, sensitive attributes such as race, gender, sexual orientation, and opinions. These opaque methods can predict behaviors for marketing purposes and influence behavior for profit, serving attention economics but also reinforcing existing biases such as gender stereotyping. Although two international human rights treaties include explicit obligations relating to harmful and wrongful stereotyping, these stereotypes persist online and offline. By identifying how inferential analytics may reinforce gender stereotyping and affect marginalized communities, opportunities for addressing these concerns and thereby increasing privacy, diversity, and inclusion online can be explored. This is important because misgendering reinforces gender stereotypes, accentuates gender binarism, undermines privacy and autonomy, and may cause feelings of rejection, impacting people's self-esteem, confidence, and authenticity. In turn, this may increase social stigmatization. This study brings into view concerns of discrimination and exacerbation of existing biases that online platforms continue to replicate and that literature starts to highlight. The implications of misgendering on Twitter are investigated to illustrate the impact of algorithmic bias on inadvertent privacy violations and reinforcement of social prejudices of gender through a multidisciplinary perspective, including legal, computer science, and critical feminist media-studies viewpoints. An online pilot survey was conducted to better understand how accurate Twitter's gender inferences of its users’ gender identities are. This served as a basis for exploring the implications of this social media practice.

在线和社交媒体平台采用自动识别方法来假设用户的偏好，种族，性别，性取向和观点等敏感属性。这些不透明的方法可以预测出于营销目的的行为，并影响为获利的行为，不仅为注意力经济学服务，而且还加强了诸如性别定型观念之类的现有偏见。尽管两项国际人权条约都明确规定了与有害和不正确的陈规定型观念有关的义务，但这些陈规定型观念在网上和线下仍然存在。通过确定推论分析如何加强性别定型观念并影响边缘化社区，可以探索解决这些问题的机会，从而增加在线隐私，多样性和包容性。这很重要，因为性别不正确加剧了性别定型观念，加剧了性别二元主义，破坏了隐私和自主权，并可能引起拒绝感，影响人们的自尊，自信和真实性。反过来，这可能会增加社会的耻辱感。这项研究考虑到歧视和加剧现有偏见的担忧，在线平台继续在复制并且文献开始突出。通过在法律，计算机科学和女权主义媒体研究的多角度研究，调查了Twitter上性别歧视的影响，以说明算法偏差对无意侵犯隐私和增强社会性别偏见的影响。进行了在线试点调查，以更好地了解Twitter对其用户性别身份的性别推断有多准确。