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      Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations

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          Abstract

          Important decisions that impact humans lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems (ADMS) can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social benefits of automation. In this article, we consider the feasibility and efficacy of ethics-based auditing (EBA) as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity’s present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS.

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          Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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            AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations

            This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.
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              How the machine ‘thinks’: Understanding opacity in machine learning algorithms

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                Author and article information

                Contributors
                jakob.mokander@oii.ox.ac.uk
                jessica.morley@oii.ox.ac.uk
                mariarosaria.taddeo@oii.ox.ac.uk
                luciano.floridi@oii.ox.ac.uk
                Journal
                Sci Eng Ethics
                Sci Eng Ethics
                Science and Engineering Ethics
                Springer Netherlands (Dordrecht )
                1353-3452
                1471-5546
                6 July 2021
                6 July 2021
                2021
                : 27
                : 4
                : 44
                Affiliations
                [1 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Oxford Internet Institute, , University of Oxford, ; 1 St Giles’, Oxford, OX1 3JS UK
                [2 ]GRID grid.36212.34, Alan Turing Institute, , British Library, ; 96 Euston Rd, London, NW1 2DB UK
                Author information
                http://orcid.org/0000-0002-8691-2582
                http://orcid.org/0000-0001-5221-4770
                http://orcid.org/0000-0002-1181-649X
                http://orcid.org/0000-0002-5444-2280
                Article
                319
                10.1007/s11948-021-00319-4
                8260507
                34231029
                8843ecac-ae91-46f4-9feb-24247ee5f499
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 February 2021
                : 8 June 2021
                Funding
                Funded by: The Welcome Trust
                Funded by: The Society of Swedish Engineers in Great Britain
                Award ID: 2021
                Award Recipient :
                Categories
                Original Research/Scholarship
                Custom metadata
                © Springer Nature B.V. 2021

                Ethics
                artificial intelligence,auditing,automated decision-making,ethics,governance
                Ethics
                artificial intelligence, auditing, automated decision-making, ethics, governance

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