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            Artificial Intelligence and the Internet of Things

            UK Policy Opportunities and Challenges

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            Author(s)
            Bunz, Mercedes
            Janciute, Laima
            Language
            English
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            Abstract
            "Through algorithms and artificial intelligence (AI), objects and digital services now demonstrate new skills they did not have before, right up to replacing human activity through pre-programming or by making their own decisions. As part of the internet of things, AI applications are already widely used today, for example in language processing, image recognition and the tracking and processing of data. This policy brief illustrates the potential negative and positive impacts of AI and reviews related policy strategies adopted by the UK, US, EU, as well as Canada and China. Based on an ethical approach that considers the role of AI from a democratic perspective and considering the public interest, the authors make policy recommendations that help to strengthen the positive impact of AI and to mitigate its negative consequences."
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/190655
            Keywords
            algorithms; artificial intelligence; internet of things; AI regulation; policy; Automation; Decision-making; European Union; Government of the United Kingdom; thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics::UBJ Digital and information technologies: social and ethical aspects
            DOI
            10.16997/book25
            ISBN
            9781911534822; 9781911534839; 9781911534846
            Publisher
            University of Westminster Press
            Publication date and place
            2018
            Series
            CAMRI Policy Briefs,
            Pages
            31
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            Credits


            • logo Investir l'avenirInvestir l'avenir
            • logo MESRIMESRI
            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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