Linking Ethnicity Targeting with Artificial Intelligence and Data Collection: Perceptions and Behavioral Responses of Black Consumers

Abstract

Abstract Data-centric targeting with artificial intelligence (AI) is transforming advertising by using machine learning and big data to target consumers, creating value for both consumers and brands. Despite the growing interest in ethnicity targeting in social media, there is still much to learn about leveraging ethnicity data for advertising research and practice. In this study, we surveyed 1,030 Black U.S. social media users to explore their understanding of AI and data gathering related to ethnicity. We focused on ethnic affinity targeting (EAT), a controversial tactic used by social media platforms. Our results indicate that the ethical aspects of persuasion knowledge, specifically appropriateness beliefs, affect consumers’ coping strategies through distinct mechanisms. Consumers’ ethnic identification and the stability of their affinity feelings toward social media also influence intentions to use specific coping strategies. These findings suggest that consumers’ perceptions of ethnicity targeting depend on how advertisers collect and use ethnicity data and underscore the importance of diverse perspectives to inform algorithm transparency practices and policies.