in: Water 12: 244 AbstractTackling diffuse pollution from agriculture is a key challenge for governments seeking to implement the European Union’s Water Framework Directive (WFD). In the research liter  

This article addresses the matter of “urban spillover” in rural population development, i.e. how urban localities tend to push a ring of diffuse urban growth outwards as they expand in area. The data

International Journal of Public Administration, Vol 40 (7), s 548-558 (2017) DOI:  http://dx.doi.org/10.1080/01900692.2016.1157693 Abstract The article analyses the orientations of political employees in

Working Paper 2016 no. 2(Published in International Journal of Public Administration, Vol 40 (7), pp 548-558  (2017) DOI:  http://dx.doi.org/10.1080/01900692.2016.1157693) This paper analyses the orient

The Embassy of Canada, in partnership with the Institute for Futures Studies (IFFS), would like to invite you to a seminar presenting the Canada-Sweden collaborative research project on violent threat Thursday 19 January 2023, 15:30-18:00 Embassy of Canada to Sweden, Raoul Wallenberg room, 7th floor, Klarabergsgatan 23, Stockholm

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.