(De)Politicizing the Digital Transformation: Assessing Issue Competition Dynamics in European National Parliaments

The sociotechnical integration of applications based on "artificial intelligence" has received an increasing amount of public and scholarly attention due to ongoing innovations, however, the study of its politicization in traditional political arenas remains limited. Most contributions in research on "AI Governance" focus on executive actors, thus neglecting the relevance of political parties for shaping AI discourses through their agenda-setting power in parliament. Through situating the debate on AI into the broader context of the digital transformation and treating it as one policy topic among many, that legislators are challenged with to facilitate and steer their national digitalization efforts, this thesis aims to analyze the factors driving the politicization of different aspects of the digital transformation in parliamentary debates. Theoretically, the contribution extends on the argument, that party ideology matters in politicizing policy issues relating to the digital transformation through incentivizing parties to sharpen their party-political profile in adjacent policy fields such as economics or civil rights, which allows them to benefit from positive "spill-over effects" (König and Wenzelburger, 2019). Furthermore, the impact of government-opposition dynamics on the salience of policy issues relating to the digital transformation will be discussed and empirically assessed. The paper contributes to research on the politics of digitalization and empirical parliamentary studies focusing on the contents of legislative speeches. By employing computational text analysis methods on newly compiled multilingual corpora of speeches from five national parliaments, namely Germany, Sweden, Norway, Denmark, and the Netherlands between 2010 and mid-2022, the contribution aims to extend the literature on multilingual research designs in political science and issue competition research. This research design further facilitates to explore whether recent innovations in machine learning (commonly referred to as "AI") have displaced other issues in public lawmakers' perceptions of states' digital transformation efforts.