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By introducing the concept of high-reach AI, this paper focuses on AI systems whose widespread use may generate significant risks for both individuals and societies. While some of those risks have been recognised under the AI Act, we analyse the rules laid down by the Digital Services Act (DSA) for recommender systems used by dominant social media platforms as a prominent example of high-reach AI. Specifically, we examine transparency provisions aimed at addressing adverse effects of these AI technologies employed by social media very large online platforms (VLOPs). Drawing from AI transparency literature, we analyse DSA transparency measures through the conceptual lens of horizontal and vertical transparency. Our analysis indicates that while the DSA incorporates transparency provisions in both dimensions, the most progressive amendments emerge within the vertical transparency, for instance, by the introduction of the systemic risk assessment mechanism. However, we argue that the true impact of the new transparency provisions extends beyond their mere existence, emphasising the critical role of oversight entities in implementation and application of the DSA. Overall, this study highlights the paramount importance of vertical transparency in providing a comprehensive understanding of the aggregated risks associated with high-reach AI technologies, exemplified by social media recommender systems., QC 20240709

This brief anthology presents the basics of the interdisciplinary course called Smart City Governance – AI Ethics in a Spatial Context, given at LTH, Lund University. Furthermore, it includes three selected essays from the main assignment written by students from the class of 2022/2023. These provide with examples of the topics possible to analyse when combining engineering students from programmes on data, ICT, architecture, and land surveying with students from the humanities or social sciences.

Transparency in artificial intelligence (AI) can mean many things, but at the same time, it is currently a central focus for both scientific and regulatory attention. We seek to critically unpack this conceptual vagueness. This is particularly called for given recent focus on transparency in much of AI policy. To this end, we construct our analysis of AI Transparency into four facets. Firstly, (1) explainability (XAI) has become an expanding field in AI, which we argue needs to be complemented by more explicit focus on the (2) mediation of AI-systems functionality, as a communicated artefact. Furthermore, in the policy discourse on AI, the importance of (3) literacy is underscored. We draw from the rich literacy literature in order to show both promising and troubling consequences of this. Lastly, we unpack transparency as a form of governance, within a (4) legal framework encompassing a structure of trade-offs. By these four facets we aim to bring more clarity to the multifaceted concept of transparency in AI.