The role of artificial intelligence in achieving the Sustainable Development Goals

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. 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.

[1]  Hojjat Adeli,et al.  Intelligent Infrastructure: Neural Networks, Wavelets, and Chaos Theory for Intelligent Transportation Systems and Smart Structures , 2008 .

[2]  Evangelos Pournaras,et al.  Society: Build digital democracy , 2015, Nature.

[3]  Stuart J. Russell,et al.  Research Priorities for Robust and Beneficial Artificial Intelligence , 2015, AI Mag..

[4]  Carter C. Price,et al.  Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations , 2013 .

[5]  Anne Lauscher.  Life 3.0: being human in the age of artificial intelligence , 2019, Internet Histories.

[6]  Ben Goertzel,et al.  Distributed, decentralized, and democratized artificial intelligence , 2019, Technological Forecasting and Social Change.

[7]  S. Thrun,et al.  Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[8]  Leo Anthony Celi,et al.  The “inconvenient truth” about AI in healthcare , 2019, npj Digital Medicine.

[9]  M. Tavoni,et al.  Connecting climate action with other Sustainable Development Goals , 2019, Nature Sustainability.

[10]  Un Desa.  Transforming our world : The 2030 Agenda for Sustainable Development , 2016 .

[11]  Erik Brynjolfsson,et al.  The second machine age: work, progress, and prosperity in a time of brilliant technologies, 1st Edition , 2014 .

[12]  Marti Petit.  Towards a Critique of Algorithmic Reason. A State-of-the-Art Review of Artificial Intelligence, Its Influence on Politics and Its Regulation , 2018 .

[13]  Roberta Kwok,et al.  AI empowers conservation biology , 2019, Nature.

[14]  Eric Horvitz,et al.  Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure , 2018, HCOMP.

[15]  Jonathan Gorman,et al.  Rights and Reason: United Nations Universal Declaration of Human Rights , 2003 .

[16]  Donata Francescato.  Globalization, artificial intelligence, social networks and political polarization: New challenges for community psychologists , 2018 .

[17]  Sang Michael Xie,et al.  Combining satellite imagery and machine learning to predict poverty , 2016, Science.

[18]  Hugh P. Possingham,et al.  Solving conservation planning problems with integer linear programming , 2016 .

[19]  anonymous,et al.  Comprehensive review , 2019 .

[20]  L. Armstrong,et al.  Application of data mining techniques for predicting rice crop yield in semi-arid climatic zone of India , 2017, 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR).

[21]  Andreas Harrer,et al.  Simulating Norms, Social Inequality, and Functional Change in Artificial Societies , 1999, J. Artif. Soc. Soc. Simul..

[22]  R. Courtland.  Bias detectives: the researchers striving to make algorithms fair , 2018, Nature.

[23]  Geraint Rees,et al.  Clinically applicable deep learning for diagnosis and referral in retinal disease , 2018, Nature Medicine.

[24]  High-Level Expert Group on Artificial Intelligence – Draft Ethics Guidelines for Trustworthy AI , 2019 .

[25]  Francesco Fuso Nerini.  Use SDGs to guide climate action , 2018 .

[26]  Richard E. Brown,et al.  Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431 , 2008 .

[27]  Aki Nagano,et al.  Economic Growth and Automation Risks in Developing Countries Due to the Transition Toward Digital Modernity , 2018, ICEGOV.

[28]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[29]  Paris Perdikaris,et al.  Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations , 2017, ArXiv.

[30]  Debabrata Das,et al.  The 2030 Agenda for Sustainable Development: Where Does India Stand? , 2019, Journal of Rural Development.

[31]  Long Seng To,et al.  Mapping synergies and trade-offs between energy and the Sustainable Development Goals , 2017, Nature Energy.

[32]  Michael Marien,et al.  Book Review: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies , 2014 .

[33]  David Jacobus Dalenberg,et al.  Preventing discrimination in the automated targeting of job advertisements , 2017, Comput. Law Secur. Rev..

[34]  Spyros Makridakis,et al.  The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms , 2017 .

[35]  Dirk Helbing.  The Automation of Society is Next: How to Survive the Digital Revolution , 2015 .

[36]  Iyad Rahwan,et al.  The social dilemma of autonomous vehicles , 2015, Science.

[37]  Ehsan Bitaraf Haghighi,et al.  Organic data centers: A sustainable solution for computing facilities , 2019 .

[38]  Adam Tauman Kalai,et al.  Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings , 2016, NIPS.

[39]  J. Drake,et al.  Pattern‐recognition ecological niche models fit to presence‐only and presence–absence data , 2014 .

[40]  J. Truby.  Decarbonizing Bitcoin: Law and policy choices for reducing the energy consumption of Blockchain technologies and digital currencies , 2018, Energy Research & Social Science.

[41]  Francesco Fuso Nerini.  Shore up support for climate action using SDGs , 2018, Nature.

[42]  Ricardo Vinuesa,et al.  Simulations and experiments of heat loss from a parabolic trough absorber tube over a range of pressures and gas compositions in the vacuum chamber , 2016 .

[43]  Ammar Belatreche,et al.  Detecting price manipulation in the financial market , 2014, 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr).

[44]  Daniel Castro,et al.  Recommendations to the European Commission on Its “Draft AI Ethics Guidelines for Trustworthy AI” , 2019 .

[45]  N. Jones.  How to stop data centres from gobbling up the world’s electricity , 2018, Nature.

[46]  A. F. Adams,et al.  The Survey , 2021, Dyslexia in Higher Education.

[47]  Sungwon Kim,et al.  Daily water level forecasting using wavelet decomposition and artificial intelligence techniques , 2015 .

[48]  Ainslie J Butler,et al.  Systematic review of expert elicitation methods as a tool for source attribution of enteric illness. , 2015, Foodborne pathogens and disease.

[49]  M. G. Morgan.  Use (and abuse) of expert elicitation in support of decision making for public policy , 2014, Proceedings of the National Academy of Sciences.

[50]  Abdolreza Mohamadi,et al.  Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran) , 2015 .

[51]  E. Hafen,et al.  Will Democracy Survive Big Data and Artificial Intelligence? , 2017, Towards Digital Enlightenment.

[52]  Ryen W. White.  Opportunities and challenges in search interaction , 2018, Commun. ACM.

[53]  Rebecca Henderson,et al.  The Impact of Artificial Intelligence on Innovation , 2018, The Economics of Artificial Intelligence.

[54]  S. M. Solaiman,et al.  Legal personality of robots, corporations, idols and chimpanzees: a quest for legitimacy , 2016, Artificial Intelligence and Law.

[55]  WestJarrod,et al.  Intelligent financial fraud detection , 2016 .

[56]  Daron Acemoglu,et al.  Artificial Intelligence, Automation and Work , 2018 .

[57]  Max Tegmark.  Life 3.0: Being Human in the Age of Artificial Intelligence , 2017 .

[58]  Margaret Kosmala,et al.  Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning , 2017, Proceedings of the National Academy of Sciences.

[59]  Maumita Bhattacharya,et al.  Intelligent Financial Fraud Detection: A Comprehensive Review , 2015 .

[60]  Petr Hájek,et al.  Mining corporate annual reports for intelligent detection of financial statement fraud - A comparative study of machine learning methods , 2017, Knowl. Based Syst..

[61]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[62]  J ButlerAinslie,et al.  Systematic review of expert elicitation methods as a tool for source attribution of enteric illness. , 2015 .

[63]  A. Ferguson.  The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement , 2017 .

[64]  Dietmar Jannach,et al.  A systematic review and taxonomy of explanations in decision support and recommender systems , 2017, User Modeling and User-Adapted Interaction.

[65]  Meryem Duygun Fethi,et al.  Assessing Bank Efficiency and Performance with Operational Research and Artificial Intelligence Techniques: A Survey , 2009 .

[66]  Evelina Trutnevyte,et al.  A Research and Innovation Agenda for Zero-Emission European Cities , 2019, Sustainability.

[67]  Dirk Helbing,et al.  An Extension of Asimov’s Robotics Laws , 2018, Towards Digital Enlightenment.

[68]  Mohammed Azmi Al-Betar,et al.  comprehensive review : Krill Herd algorithm ( KH ) and its pplications saju , 2016 .

[69]  Lone Koefoed Hansen,et al.  Intimate Futures: Staying with the Trouble of Digital Personal Assistants through Design Fiction , 2018, Conference on Designing Interactive Systems.

[70]  D. Helbing,et al.  Unintended Side Effects of the Digital Transition: European Scientists’ Messages from a Proposition-Based Expert Round Table , 2018, Sustainability.

[71]  Paris Perdikaris,et al.  Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations , 2017, ArXiv.

[72]  Chris T. Kiranoudis,et al.  Automatic identification of oil spills on satellite images , 2006, Environ. Model. Softw..

[73]  S. Wegren,et al.  The “left behind”: Smallholders in contemporary Russian agriculture , 2018, Journal of Agrarian Change.

[74]  Richard Lance Keeble,et al.  The myth of freedom , 2020 .

[75]  Arjan Durresi,et al.  A survey: Control plane scalability issues and approaches in Software-Defined Networking (SDN) , 2017, Comput. Networks.