The Myth of Algorithmic Regulation: An ethnographic exploration of algorithms, actors, and institutions

A growing number of public organizations adopt algorithms in the hope that organizational practices will become more effective and efficient. Especially for regulatory agencies, algorithms are believed to have a high potential to further rationalize practices. Such practices are known as algorithmic regulation. Yet, realizing the potentials of algorithmic regulation is not easy and often does not result in desired outcomes. In my dissertation, I have explored the potentials of algorithmic regulation and how regulatory agencies attempt to realize them mainly through ethnographic fieldwork at two regulatory agencies in the Netherlands. To make sense of the results, this dissertation offers a new lens on the adoption of algorithms: the myth of algorithmic regulation. It enables us to better differentiate between algorithmic myth and organizational reality and to better understand the process through which the myth is translated into reality and the actors involved. The research shows that the process of adopting algorithms involves three specific organizational-institutional patterns: decoupling, learning, and integrating. First, decoupling helps to understand why the adoption of algorithms does not always lead to changes in regulatory practices. When actors who work on the adoption of algorithms do not overcome conflicting understandings of meanings, norms, and power relations, algorithms may become decoupled from everyday practice. Second, learning describes two ways of realizing change in organizations that adopt algorithms: single-loop learning when data science is being connected to other forms of expertise and double-loop learning when institutional mechanisms are being established for algorithmization. Third, with the observed pattern of integration I refer to how public sector data scientists react to varying and potentially conflicting institutional logics. The research shows that data scientists integrate a technological logic with domain and political-admini