Pretty Hurts: The Intersectionality of Race, Weight and Socioeconomic Status on Algorithmic Bias

Abstract

Algorithms are not neutral, they are informed by the systemic biases and white norms that exist within our world. I argue that societal norms about blackness and beauty have influenced how algorithms operate on social media platforms. I begin with a review of the relevant literature on the association between blackness and mistrust, racialized beauty standards and stereotypical media representations of Black people to construct a foundation for the societal factors that influence technology. I then give an overview of algorithmic bias that renders Black people invisible on social media platforms. I proceed to my qualitative analysis about content creator’s perceptions of how algorithms impact creators, and especially the trajectories of Black influencers. My findings reveal that algorithms discriminate against Black creators on the basis of race, weight and socioeconomic status through content moderation that reduces the likelihood of virality for Black creators. Finally, I discuss the relevance of my findings and offer recommendations on how social media platforms can improve equity by increasing transparency.