Infrastructures of listening: AI and the reconfiguration of community-centered newswork
TL;DR
Results show that AI functioned less as a tool for automation than as an infrastructural layer supporting collective listening and cross-newsroom sense-making in the lead-up to the 2024 U.S. election.
Abstract
This study examines how AI-supported tools can reshape journalistic epistemologies, labor practices, and community relationships through an in-depth qualitative analysis of “Listening Across Divides,” a collaborative project conducted by NPR in the lead-up to the 2024 U.S. election. Drawing on interviews with journalists, editors, and project staff across nine public media stations, the study explores how AI-mediated systems designed to record, organize, and surface themes from facilitated community conversations intersected with engagement-oriented journalism practices. Findings show that AI functioned less as a tool for automation than as an infrastructural layer supporting collective listening and cross-newsroom sense-making. The study demonstrates how technologies associated with efficiency and scale may simultaneously support and reshape slower forms of community-centered journalism rooted in sustained listening. In doing so, the article contributes to scholarship on engagement journalism, AI in newswork, and journalistic knowledge production by conceptualizing AI as a sociotechnical infrastructure for mediated collective sense-making.