Binary Gender

Research on binary gender is increasingly challenging its limitations, focusing on how existing models and systems perpetuate biases against non-binary individuals. Current studies utilize large language models and multimodal models like CLIP to analyze gender bias in areas such as machine translation, name prediction, and image labeling, revealing significant disparities in accuracy and representation for non-binary genders. This work highlights the urgent need for more inclusive algorithms and datasets, impacting the development of fairer and more equitable AI systems across various applications. The ultimate goal is to move beyond binary classifications to create technology that accurately and respectfully reflects the diversity of gender identities.