Influencer Management Tools: Algorithmic Cultures, Brand Safety, and Bias

Abstract

This article explores algorithmic influencer management tools, designed to support marketers in selecting influencers for advertising campaigns, based on categorizations such as brand suitability, “brand friendliness,” and “brand risk.” I argue that, by approximating these values, tools reify existing social inequalities in influencer industries, particularly along the lines of sexuality, class, and race. They also deepen surveillance of influencer content by brand stakeholders, who are concerned that influencers will err and be “cancelled” (risking their investments in content). My critical framework synthesizes feminist critiques of ostensibly participatory influencer industries with close attention to critical algorithmic studies. This article provides an in-depth look at how brand risk and brand safety are predicted and measured using one tool, Peg. Through a “walk through” of this tool, underpinned by a wider industry ethnography, I demonstrate how value-laded algorithmic judgments map onto well-worn hierarchies of desirability and employability that originate from systemic bias along the lines of class, race, and gender.