"Knowing the Game: Motivations and Skills Among Partisan Policy Professionals", Journal of professions and organizations, Advance Access published September 21, 2016, doi: 10.1093/jpo/jow008 Abstract This

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In Hanemaayer, A. (editor) Artificial Intelligence and Its Discontents. Palgrave. Abstract AI has often reached individuals covertly, rather than by their own choosing. Standard automatic version update

Journal of European Public Policy Abstract Christian Democratic socio-economic ideology underwent a paradigm shift through the Europeanization of its party networks. Christian Democratic networks starte

in: Technology in Society. 63, 101396 Abstract:Artificial Intelligence (AI) in the form of Deep Learning (DL) technology has diffused in the consumer domain in a unique way as compared to previous gene, i.e., by being added to preexisting technologies that are already in use. We find that DL-algorithms for recommendations or ranking have been infused into all the 15 most popular mobile applications (apps) in the U.S. (as of May 2019). DL-infusion enables fast and vast diffusion. For example, when a DL-system was infused into YouTube, it almost immediately reached a third of the world's population. We argue that existing theories of innovation diffusion and adoption have limited relevance for DL-infusion, because it is a process that is driven by enterprises rather than individuals. We also discuss its social and ethical implications. First, consumers have a limited ability to detect and evaluate an infused technology. DL-infusion may thus help to explain why AI's presence in society has not been challenged by many. Second, the DL-providers are likely to face conflicts of interest, since consumer and supplier goals are not always aligned. Third, infusion is likely to be a particularly important diffusion process for DL-technologies as compared to other innovations, because they need large data sets to function well, which can be drawn from preexisting users. Related, it seems that larger technology companies comparatively benefit more from DL-infusion, because they already have many users. This suggests that the value drawn from DL is likely to follow a Matthew Effect of accumulated advantage online: many preexisting users provide a lot of behavioral data, which bring about better DL-driven features, which attract even more users, etc. Such a self-reinforcing process could limit the possibilities for new companies to compete. This way, the notion of DL-infusion may put light on the power shift that comes with the presence of AI in society.