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      The role of artificial intelligence in achieving the Sustainable Development Goals

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          Abstract

          The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.

          Abstract

          Artificial intelligence (AI) is becoming more and more common in people’s lives. Here, the authors use an expert elicitation method to understand how AI may affect the achievement of the Sustainable Development Goals.

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          Most cited references44

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          Mapping synergies and trade-offs between energy and the Sustainable Development Goals

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            The social dilemma of autonomous vehicles

            Autonomous vehicles (AVs) should reduce traffic accidents, but they will sometimes have to choose between two evils, such as running over pedestrians or sacrificing themselves and their passenger to save the pedestrians. Defining the algorithms that will help AVs make these moral decisions is a formidable challenge. We found that participants in six Amazon Mechanical Turk studies approved of utilitarian AVs (that is, AVs that sacrifice their passengers for the greater good) and would like others to buy them, but they would themselves prefer to ride in AVs that protect their passengers at all costs. The study participants disapprove of enforcing utilitarian regulations for AVs and would be less willing to buy such an AV. Accordingly, regulating for utilitarian algorithms may paradoxically increase casualties by postponing the adoption of a safer technology.
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              The mythos of model interpretability

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                Author and article information

                Contributors
                rvinuesa@mech.kth.se
                francesco.fusonerini@energy.kth.se
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 January 2020
                13 January 2020
                2020
                : 11
                : 233
                Affiliations
                [1 ]Linné FLOW Centre, KTH Mechanics, SE-100 44 Stockholm, Sweden
                [2 ]ISNI 0000000121581746, GRID grid.5037.1, Division of Robotics, Perception, and Learning, School of EECS, , KTH Royal Institute Of Technology, ; Stockholm, Sweden
                [3 ]ISNI 0000000121581746, GRID grid.5037.1, Division of Media Technology and Interaction Design, , KTH Royal Institute of Technology, ; Lindstedtsvägen 3, Stockholm, Sweden
                [4 ]ISNI 0000 0001 1034 3451, GRID grid.12650.30, Responsible AI Group, Department of Computing Sciences, , Umeå University, ; SE-90358 Umeå, Sweden
                [5 ]ISNI 0000 0001 2108 8097, GRID grid.419247.d, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, ; Müggelseedamm 310, 12587 Berlin, Germany
                [6 ]AI Sustainability Center, SE-114 34 Stockholm, Sweden
                [7 ]ISNI 0000 0001 2002 0998, GRID grid.423984.0, Basque Centre for Climate Change (BC3), ; 48940 Leioa, Spain
                [8 ]ISNI 0000 0004 1936 7830, GRID grid.29980.3a, Department of Zoology, , University of Otago, ; 340 Great King Street, 9016 Dunedin, New Zealand
                [9 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, Center for Brains, Minds and Machines, , Massachusetts Institute of Technology, ; Cambridge, Massachusetts 02139 USA
                [10 ]ISNI 0000000121581746, GRID grid.5037.1, Unit of Energy Systems Analysis (dESA), , KTH Royal Institute of Technology, ; Brinellvagen, 68SE-1004 Stockholm, Sweden
                Author information
                http://orcid.org/0000-0001-6570-5499
                http://orcid.org/0000-0001-5211-6388
                http://orcid.org/0000-0002-8127-9335
                http://orcid.org/0000-0002-4770-4051
                Article
                14108
                10.1038/s41467-019-14108-y
                6957485
                31911652
                60dbbb1a-51e6-44b7-b7c5-f50f7ce97ec2
                © The Author(s) 2020

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 May 2019
                : 16 December 2019
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                © The Author(s) 2020

                Uncategorized
                computational science,developing world,energy efficiency
                Uncategorized
                computational science, developing world, energy efficiency

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