From Epistemology to Practice: How Datafication and AI Shape Inequities in Journalism

Abstract

Datafication and artificial intelligence have changed the ways journalists gather information, decide what stories they should pursue, how to produce and distribute news, and commodify products. Technological innovations have been used in journalism to hold power to account, expose injustices, and understand audiences’ diverse information needs and interests. However, they also have implications for power relationships in newsrooms, between news organizations, between news organizations and tech corporations, and between journalists and their audiences. Against this backdrop, this dissertation asks how datafication and AI structure journalism’s ways of knowing (epistemology) and ways of doing (practice), and what their implications for equity are. The central argument is that inequalities and socio-technological change in journalism are not separate issues. Rather, socio-technological change should be understood as shaping power dynamics, as it restructures journalism’s way of knowing and doing. In some contexts, this has created new opportunities to address and reduce inequities in journalism. In others, it has amplified existing power asymmetries. The dissertation contributes to a more thorough understanding of the underlying logics and conditions of these processes. Empirically, it comprises four studies, using semi-structured interviews, ethnographic observations, as well as qualitative and quantitative textual analysis. It draws on experiences and perspectives of 87 news professionals situated in 27 countries in Europe, the Americas, and the APAC region. Applying a critical data perspective, the work explicitly foregrounds questions of power and discusses socio- technological change in journalism on three levels: the individual, the organizational, and the socio-institutional.