TY - JOUR

T1 - The Algorithmic Gut Feeling

T2 - Epistemologies of data in AI-driven News Distribution

AU - Hartley, Jannie Møller

AU - Thylstrup, Nanna

PY - 2024

Y1 - 2024

N2 - This article explores the epistemic practices and doxa of data workers in a news organisation in Denmark that is currently developing and experimenting with artificial intelligence (AI)-driven recommender systems, machine learning and natural language processing solutions. Previous literature on the changing epistemologies of digital journalism has focused on the increased role of metrics and the transformed practices inside newsrooms, as well as on how journalists perceive and articulate the computational. This article advances these studies by focusing on how data scientists perceive and articulate “the journalistic” when building AI systems for distributing news. Developing the notion of “the algorithmic gut feeling”, the article highlights different frictions present in the articulations of the journalistic doxa in AI-driven data work concerning (1) how to algorithmically define ethics, (2) how to algorithmically categorise and understand relevance, and (3) how to algorithmically curate “a good mix” for the front page. The emerging frictions and algorithmic gut feeling are key to understanding how the doxa of data workers involved and deeply invested in “the good of journalism” at times also transforms journalistic epistemologies of what constitutes “news” and “the right mix” of content in the service of a democratic public.

AB - This article explores the epistemic practices and doxa of data workers in a news organisation in Denmark that is currently developing and experimenting with artificial intelligence (AI)-driven recommender systems, machine learning and natural language processing solutions. Previous literature on the changing epistemologies of digital journalism has focused on the increased role of metrics and the transformed practices inside newsrooms, as well as on how journalists perceive and articulate the computational. This article advances these studies by focusing on how data scientists perceive and articulate “the journalistic” when building AI systems for distributing news. Developing the notion of “the algorithmic gut feeling”, the article highlights different frictions present in the articulations of the journalistic doxa in AI-driven data work concerning (1) how to algorithmically define ethics, (2) how to algorithmically categorise and understand relevance, and (3) how to algorithmically curate “a good mix” for the front page. The emerging frictions and algorithmic gut feeling are key to understanding how the doxa of data workers involved and deeply invested in “the good of journalism” at times also transforms journalistic epistemologies of what constitutes “news” and “the right mix” of content in the service of a democratic public.

KW - Faculty of Humanities

U2 - 10.1080/21670811.2024.2319641

DO - 10.1080/21670811.2024.2319641

M3 - Journal article

JO - Digital Journalism

JF - Digital Journalism

SN - 2167-0811

ER -