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        Ethics of Artificial Intelligence

        Case Studies and Options for Addressing Ethical Challenges

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        Author(s)
        Stahl, Bernd Carsten
        Schroeder, Doris
        Rodrigues, Rowena
        Language
        English
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        Abstract
        This open access collection of AI ethics case studies is the first book to present real-life case studies combined with commentaries and strategies for overcoming ethical challenges. Case studies are one of the best ways to learn about ethical dilemmas and to achieve insights into various complexities and stakeholder perspectives. Given the omnipresence of AI ethics in academic, policy and media debates, the book will be suitable for a wide range of audiences, from scholars of different disciplines (e.g. AI science, ethics, politics, philosophy, economics) to policy-makers, lobbying NGOs, teachers and the educated public.
        URI
        https://library.oapen.org/handle/20.500.12657/59315
        Keywords
        Artificial intelligence, AI; Ethics of technology; Computer ethics; Information ethics; Responsible research and innovation
        DOI
        10.1007/978-3-031-17040-9
        ISBN
        9783031170409, 9783031170409
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Cham, 2023
        Imprint
        Springer International Publishing
        Series
        SpringerBriefs in Research and Innovation Governance,
        Classification
        Ethics and moral philosophy
        Artificial intelligence
        Philosophy
        Digital and information technologies: social and ethical aspects
        Pages
        116
        Rights
        http://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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