Intersectional Group

Intersectional group analysis examines how multiple social categories (e.g., race, gender) combine to create unique experiences and disparities, particularly within AI systems and data representation. Current research focuses on developing methods to accurately measure and mitigate biases affecting these groups, employing techniques like structured regression for performance evaluation and novel metrics such as Multi-Group Proportional Representation to ensure fair representation in datasets and algorithms. This work is crucial for identifying and addressing systemic inequalities amplified by technology, improving the fairness and accuracy of AI systems, and fostering more equitable representation across diverse populations.