Elucidating AI Policy Discourse : Uncovering Themes Through Latent Dirichlet Allocation

Abstract: This thesis embarks on a journey to investigate the discourse contained within the policy documents examined by utilizing the topic modeling technique labeled Latent Dirichlet Allocation. The aforementioned investigation will be conducted through the theoretical lens of Systems Theory and Discourse Analysis Theory. The thesis aims to identify the core constituents, form a consensus and enrich the scientific communities’ understanding regarding how these core constituents alongside the discourse contained within the policy documents shape the overall landscape of AI governance in continental Europe. Furthermore, prior to an in depth investigation of the methods and theoretical frameworks mentioned above commences, an introduction is presented to give additional insight to the background of AI & the problem formulation. The results of this study reveal 8 inferred themes. These inferred themes are then thoroughly discussed in alignment with the principles and concepts set forth by the theoretical frameworks. The thesis then provides a conclusive penultimate subchapter that encapsulates the key points and directly addresses the research question before highlighting possible future research opportunities.