Unblackboxing How Sociotemporalities Inform AI Accountability: The Case of Targeted Advertising

Abstract

In recent years, numerous accountability interventions have been introduced to address the harms and inequalities associated with Artificial Intelligence (AI) systems. Early efforts concentrated on transparency and explainability, often operationalized as technical fixes intended to “open the black box” and render algorithmic processes more intelligible. However, sociological research has revealed the limitations of these interventions, particularly their narrow focus on the technical and operational dimensions of AI. In response, sociologists have broadened the scope of “unblackboxing” to include the sociomaterialities of AI, or the social, political, and environmental relations that both shape and are shaped by AI technologies and their infrastructures. This article extends this agenda by focusing on sociotemporalities: the narratives and structures of time that shape both AI systems and the interventions meant to improve their accountability. We re-analyze interviews from a retrospective study of transparency in targeted advertising, a domain long associated with concerns about privacy, discrimination and opaque practices. Drawing on the sociology of time, and especially the sociology of the future, we examine the sociotemporalities that actively shaped the development of targeted advertising technologies at the time and influenced key informants’ thinking about approaches to improve accountability. Our analysis suggests that sociotemporalities exerted a structuring influence on how “appropriate” accountability interventions were imagined and enacted, ultimately shaping the emergent present of targeted advertising. We discuss the application of such an approach in the context of emerging AI technologies and AI accountability interventions. We conclude by arguing that expanding unblackboxing to include sociotemporal as well as sociomaterial dimensions can help open new pathways for designing and implementing more practical, effective, and context-specific AI accountability interventions.