Algorithmic authority: How work with epistemic technologies influences occupational relations

Algorithmic authority: How work with epistemic technologies influences occupational relations

Algorithmic authority: How work with epistemic technologies influences occupational relations 

Lauren Waardenburg, Anastasia Sergeeva, & Marleen Huysman – Vrije Universiteit Amsterdam – division: business 

In parallel to developments in big data and artificial intelligence, organization and management scholars are beginning to renew their attention to how epistemic technologies influence work. Epistemic technologies are “tools that play a central role in the ongoing construction of knowledge” (Anthony, 2018, p. 661). Research has shown that using epistemic technologies results in, for example, the need for increased coordination work to transfer situated knowledge (Bailey, Leonardi, & Barley, 2012), changing work tasks and occupational identity (Nelson & Irwin, 2014) and increased demands on balancing objectivity and subjectivity in knowledge work (Schultze, 2000). Due to the growth of newer and more powerful types of epistemic technologies, more research is now needed to understand how epistemic technologies influence occupational work and specifically occupational relations (Faraj, Pachidi, & Sayegh, 2018). 

To address this question, we report on an ongoing ethnographic study at the Dutch Police, following how the police develops and uses predictive analytics. Within the police, predictive analytics is referred to as “predictive policing” – the use of analytics to predict, for example, where and when crime is likely to occur. It was introduced in the Dutch police in 2013 and is today used across nearly all 168 police stations in the Netherlands. The general aim of using predictive policing is to facilitate a change in the nature of police work towards more data-driven and efficient policing and in such a way to prevent crime from happening. 

For this study, we build on our ethnographic work which spans across 2 years and 7 months (October 2016 – April 2019). We thematically analyzed 562 hours of observation of the intelligence department of the Dutch police and 20 formal semi-structured interviews. Our findings indicate that the introduction of predictive policing was followed by a change in authority relations between the occupational groups of police officers, police management, and intelligence officers. The group of intelligence officers became increasingly influential in coordinating police work. Our process analysis finds that the increase in authority was due to intelligence officers’ developing representational expertise, i.e. skills by which they were able to construct, filter, interpret, and represent data outputs to relevant audiences. Taking on pieces of contextual knowledge of police officers, intelligence officers first employed selective aspects of the physical realm of police work to be able to infuse legitimacy in their symbolic representations of predictive policing output. However, over time the intelligence officers acquired enough authority to do without the police officers’ knowledge of the physical realm altogether. Our findings contribute to the literature on technological change and occupational relations in the age of artificial intelligence by showing that the occupational group that is in charge of interpretation work may grow in its influence and status. Given heated debates over the changing nature of jobs and the rise of powerful occupations, such as data scientists, our study offers an in-depth perspective on how authority emerges not only from developing algorithms, but importantly from everyday work with algorithms. 


Anthony, C. (2018). To question or accept? How status differences influence responses to new epistemic 

technologies in knowledge work. Academy of Management Review, 43(4), 661679. Bailey, D. E., Leonardi, P. M., & Barley, S. R. (2012). The Lure of the Virtual. Organization Science

23(5), 1485–1504. Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and Organizing in the Age of the Learning 

Algorithm. Information and Organization, 28(1), 62–70. Nelson, A. J., & Irwin, J. (2014). “Defining What We Do—All Over Again”: Occupational Identity, Technological Change, and the Librarian/Internet-Search Relationship. Academy of Management Journal, 57(3), 892–928. 


Schultze, U. (2000). A confessional account of an ethnography about knowledge work. MIS Quarterly

24(1), 341.