Overview

Apply more inclusive and socially just data labelling methodologies such as Intersectional Labeling Methodology to address gender bias. Rather than relying on static, binary gender in a face classification infrastructure, application designers should embrace and demand improvements, to feature-based labelling. For instance, labels based on neutral performative markers (e.g., beard, makeup, dress) could replace gender classification in the facial analysis model, allowing third parties and individuals who come into contact with facial analysis applications to embrace their own interpretations of those features. Instead of focusing on improving methods of gender classification, application designers could use labelling alongside other qualitative data such as Instagram captions to formulate more precise notions about user identity.