A Framework for Classifying Uncertainties in AI-Driven Newsrooms

This study examines how generative artificial intelligence (GAI) is adopted and integrated into the newsroom of a major Brazilian media organization—Globo. While AI deployment in journalism has expanded globally, the experiences lived by journalists in contexts that are not Western, educated, industrialized, rich, and democratic remain underexplored. Combining in-depth interviews, internal documents, organizational presentations, public materials, and direct observation within a qualitative case study, this study analyzes how GAI is reshaping newsroom practices, routines, and professional identities. It introduces a framework for classifying uncertainties in AI-driven newsrooms, a type II theory, which is designed to describe, classify, and organize the main forms of uncertainty faced by journalists when interacting with GAI systems, and then structure these uncertainties into three analytical levels—macro (ecosystem), meso (organization), and micro (identity)—while pairing each level with its associated coping strategies. Given its single-case basis, the framework is presented as a possible framework whose analytical categories require further empirical application before broader validation.