Leveraging Text Guidance for Enhancing Demographic Fairness in Gender Classification

This article, sourced from ArXiv, focuses on improving fairness in gender classification using text guidance. The core idea likely involves using textual information to mitigate biases that might arise in the classification process, potentially leading to more equitable outcomes across different demographic groups. The research area is relevant to the broader discussion of AI ethics and responsible AI development.