Joy highlights the critical need for an intersectional lens in evaluating machine learning technologies, particularly in facial recognition. She emphasizes that relying on aggregate performance metrics can obscure significant disparities, especially for marginalized groups. The importance of meticulous data labeling is underscored, revealing how assumptions about identity can shape outcomes in algorithmic performance.
