10. Our approach to human rights and responsible product development implement technical mitigations to reduce the chance of the problematic content associations occurring on our apps that use AI models. • Assess fairness • One way we address AI fairness is through the creation and distribution of more diverse datasets. We also, through our Casual Conversations v2 project, designed a large, consentdriven database to measure algorithmic bias and robustness. • Meta AI Research began exploring new public fairness indicators to quantitatively assess three well-documented types of potential harms and biases in computer vision models. These fairness indicators complement existing approaches to responsible AI development, such as data and model documentation. The indicators are specifically designed to adapt and evolve as research advances and new approaches emerge. We also made advances in SEER (SElf-SupERvised), Meta AI Research’s self-supervised computer vision model that is focused on improving results for diverse image sets — without the need for the careful data curation and labeling that goes into conventional computer vision training. Human rights report 52