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Short abstract:

This article explores machine learning's impact on news production, focusing on data annotation practices in a Danish news organization. Through ethnographic study, it examines how data workers shape editorial processes, revealing tensions and negotiations.

Long abstract:

This article delves into the intersection of machine learning models and news production, focusing on data annotation practices within a Danish news organization. Through an ethnographic lens, the study examines how data workers (data annotators, model builders and knowledge brokers) within a news organization navigate the integration of AI-driven solutions into the editorial process. Drawing on six months of fieldwork conducted in the "backroom of data science," the research sheds light on the intricacies of articulating, aligning, and challenging editorial values through the development of transformer models for news production.

Methodologically, the paper zooms in on the in-house manual annotation process of datasets used for article generation and recommender systems, highlighting the uncertainties, frictions, and negotiations inherent in these procedures. Theoretical underpinnings stem from critical data studies and Science and Technology Studies (STS) frameworks, specifically focusing on "critical dataset studies", the "logic of domains," and the concept of "science frictions." By leveraging these frameworks, the study elucidates the complexities that arise when different domains, such as journalism and data science, converge.

The findings contribute to our understanding of the ways in which data annotators negotiate the tension between reflecting and shaping the world, shedding light on the ways in which editorial values are negotiated and redefined in the digital age. Furthermore the paper contributes to work that sheds light on the backstage micro-decisions that shape contemporary news production in a digital age.