The role of artificial intelligence in achieving the Sustainable Development Goals

Nature Communications - Tập 11 Số 1
Ricardo Vinuesa1, Hossein Azizpour2, Iolanda Leite2, Madeline Balaam3, Virginia Dignum4, Sami Domisch5, Anna Felländer6, Simone D. Langhans7, Max Tegmark8, Francesco Fuso Nerini9
1Linné FLOW Centre, KTH Mechanics, SE-100 44 Stockholm, Sweden
2Division of Robotics, Perception, and Learning, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden
3Division of Media Technology and Interaction Design, KTH Royal Institute of Technology, Lindstedtsvägen 3, Stockholm, Sweden
4Responsible AI Group, Department of Computing Sciences, Umeå University, SE-90358, Umeå, Sweden
5Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany
6AI Sustainability Center, SE-114 34, Stockholm, Sweden
7Basque Centre for Climate Change, BC3, 48940 Leioa, Spain
8Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA
9Unit of Energy Systems Analysis (dESA), KTH Royal Institute of Technology, Brinellvagen, 68SE-1004, Stockholm, Sweden

Tóm tắt

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.

Từ khóa


Tài liệu tham khảo

Acemoglu, D. & Restrepo, P. Artificial Intelligence, Automation, and Work. NBER Working Paper No. 24196 (National Bereau of Economic Research, 2018).

Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V. & Kalai, A. Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. Adv. Neural Inf. Process. Syst. 29, 4349–4357 (2016).

Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl Acad. Sci. USA 115, E5716–E5725 (2018).

Tegmark, M. Life 3.0: Being Human in the Age of Artificial Intelligence (Random House Audio Publishing Group, 2017).

Jean, N. et al. Combining satellite imagery and machine learning to predict poverty. Science (80-.) 353, 790–794 (2016).

Courtland, R. Bias detectives: the researchers striving to make algorithms fair. Nature 558, 357–360 (2018).

UN General Assembly (UNGA). A/RES/70/1Transforming our world: the 2030 Agenda for Sustainable Development. Resolut 25, 1–35 (2015).

Fuso Nerini, F. et al. Mapping synergies and trade-offs between energy and the Sustainable Development Goals. Nat. Energy 3, 10–15 https://doi.org/10.1038/s41560-017-0036-5 (2017).

Fuso Nerini, F. et al. Connecting climate action with other Sustainable Development Goals. Nat. Sustain. 1, 674–680 (2019). https://doi.org/10.1038/s41893-019-0334-y

Fuso Nerini, F. et al. Use SDGs to guide climate action. Nature 557, https://doi.org/10.1038/d41586-018-05007-1 (2018).

United Nations Economic and Social Council. Sustainable Development (United Nations Economic and Social Council, 2019).

Stockholm Resilience Centre’s (SRC) contribution to the 2016 Swedish 2030 Agenda HLPF report (Stockholm University, 2017).

International Energy Agency. Digitalization & Energy (International Energy Agency, 2017).

Fuso Nerini, F. et al. A research and innovation agenda for zero-emission European cities. Sustainability 11, 1692 https://doi.org/10.3390/su11061692 (2019).

Jones, N. How to stop data centres from gobbling up the world’s electricity. Nature 561, 163–166 (2018).

Truby, J. Decarbonizing Bitcoin: law and policy choices for reducing the energy consumption of Blockchain technologies and digital currencies. Energy Res. Soc. Sci. 44, 399–410 (2018).

Ahmad Karnama, Ehsan Bitaraf Haghighi, Ricardo Vinuesa, (2019) Organic data centers: A sustainable solution for computing facilities. Results in Engineering 4:100063

Raissi, M., Perdikaris, P. & Karniadakis, G. E. Physics informed deep learning (part I): data-driven solutions of nonlinear partial differential equations. arXiv:1711.10561 (2017).

Nagano, A. Economic growth and automation risks in developing countries due to the transition toward digital modernity. Proc. 11th International Conference on Theory and Practice of Electronic Governance—ICEGOV ’18 (2018). https://doi.org/10.1145/3209415.3209442

Helbing, D. & Pournaras, E. Society: build digital democracy. Nature 527, 33–34 (2015).

Helbing, D. et al. in Towards Digital Enlightenment 73–98 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-319-90869-4_7

Nagler, J., van den Hoven, J. & Helbing, D. in Towards Digital Enlightenment 41–46 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-319-90869-4_5

Wegren, S. K. The “left behind”: smallholders in contemporary Russian agriculture. J. Agrar. Chang. 18, 913–925 (2018).

NSF - National Science Foundation. Women and Minorities in the S&E Workforce (NSF - National Science Foundation, 2018).

Helbing, D. The automation of society is next how to survive the digital revolution; version 1.0 (Createspace, 2015).

Cockburn, I., Henderson, R. & Stern, S. The Impact of Artificial Intelligence on Innovation (NBER, 2018). https://doi.org/10.3386/w24449

Seo, Y., Kim, S., Kisi, O. & Singh, V. P. Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. J. Hydrol. 520, 224–243 (2015).

Adeli, H. & Jiang, X. Intelligent Infrastructure: Neural Networks, Wavelets, and Chaos Theory for Intelligent Transportation Systems and Smart Structures (CRC Press, 2008).

Nunes, I. & Jannach, D. A systematic review and taxonomy of explanations in decision support and recommender systems. Use. Model Use. Adapt Interact. 27, 393–444 (2017).

Bissio, R. Vector of hope, source of fear. Spotlight Sustain. Dev. 77–86 (2018).

Brynjolfsson, E. & McAfee, A. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (W. W. Norton & Company, 2014).

Dobbs, R. et al. Poorer Than Their Parents? Flat or Falling Incomes in Advanced Economies (McKinsey Global Institute, 2016).

Francescato, D. Globalization, artificial intelligence, social networks and political polarization: new challenges for community psychologists. Commun. Psychol. Glob. Perspect. 4, 20–41 (2018).

Saam, N. J. & Harrer, A. Simulating norms, social inequality, and functional change in artificial societies. J. Artificial Soc.Social Simul. 2 (1999).

Dalenberg, D. J. Preventing discrimination in the automated targeting of job advertisements. Comput. Law Secur. Rev. 34, 615–627 (2018).

World Economic Forum (WEF). Fourth Industrial Revolution for the Earth Series Harnessing Artificial Intelligence for the Earth (World Economic Forum, 2018).

Vinuesa, R., Fdez. De Arévalo, L., Luna, M. & Cachafeiro, H. Simulations and experiments of heat loss from a parabolic trough absorber tube over a range of pressures and gas compositions in the vacuum chamber. J. Renew. Sustain. Energy 8 (2016).

Keramitsoglou, I., Cartalis, C. & Kiranoudis, C. T. Automatic identification of oil spills on satellite images. Environ. Model. Softw. 21, 640–652 (2006).

Mohamadi, A., Heidarizadi, Z. & Nourollahi, H. Assessing the desertification trend using neural network classification and object-oriented techniques. J. Fac. Istanb. Univ. 66, 683–690 (2016).

Kwok, R. AI empowers conservation biology. Nature 567, 133–134 (2019).

Bonnefon, J.-F., Shariff, A. & Rahwan, I. The social dilemma of autonomous vehicles. Science 352, 1573–1576 (2016).

De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med 24, 1342–1350 (2018).

Russell, S., Dewey, D. & Tegmark, M. Research priorities for robust and beneficial artificial intelligence. AI Mag. 34, 105–114 (2015).

World Economic Forum (WEF). The New Physics of Financial Services – How Artificial Intelligence is Transforming the Financial Ecosystem (World Economic Forum, 2018).

Gandhi, N., Armstrong, L. J. & Nandawadekar, M. Application of data mining techniques for predicting rice crop yield in semi-arid climatic zone of India. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR) (2017). https://doi.org/10.1109/tiar.2017.8273697

Esteva, A. et al. Corrigendum: dermatologist-level classification of skin cancer with deep neural networks. Nature 546, 686 (2017).

Cao, Y., Li, Y., Coleman, S., Belatreche, A. & McGinnity, T. M. Detecting price manipulation in the financial market. 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (2014). https://doi.org/10.1109/cifer.2014.6924057

Nushi, B., Kamar, E. & Horvitz, E. Towards accountable AI: hybrid human-machine analyses for characterizing system failure. arXiv:1809.07424 (2018).

Beyer, H. L., Dujardin, Y., Watts, M. E. & Possingham, H. P. Solving conservation planning problems with integer linear programming. Ecol. Model. 328, 14–22 (2016).

Whittaker, M. et al. AI Now Report 2018 (AI Now Institute, 2018).

Petit, M. Towards a critique of algorithmic reason. A state-of-the-art review of artificial intelligence, its influence on politics and its regulation. Quad. del CAC 44 (2018).

Scholz, R. et al. Unintended side effects of the digital transition: European scientists’ messages from a proposition-based expert round table. Sustainability 10, 2001 (2018).

Ramirez, E., Brill, J., Maureen, K., Wright, J. D. & McSweeny, T. Data Brokers: A Call for Transparency and Accountability (Federal Trade Commission, 2014).

Panch, T., Mattie, H. & Celi, L. A. The “inconvenient truth” about AI in healthcare. npj Digit. Med 2, 77 (2019).

Solaiman, S. M. Legal personality of robots, corporations, idols and chimpanzees: a quest for legitimacy. Artif. Intell. Law 25, 155–179 (2017).

West, J. & Bhattacharya, M. Intelligent financial fraud detection: a comprehensive review. Comput. Secur 57, 47–66 (2016).

Hajek, P. & Henriques, R. Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods. Knowl.-Based Syst. 128, 139–152 (2017).

Perry, W. L., McInnis, B., Price, C. C., Smith, S. C. & Hollywood, J. S. Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations (RAND Corporation, 2013).

Gorr, W. & Neill, D. B. Detecting and preventing emerging epidemics of crime. Adv. Dis. Surveillance 4, 13 (2007).

IEEE. Ethically Aligned Design - Version II overview (2018). https://doi.org/10.1109/MCS.2018.2810458

European Commission. Draft Ethics Guidelines for Trustworthy AI (Digital Single Market, 2018).

Lipton, Z. C. The mythos of model interpretability. Commun. ACM 61, 36–43 (2018).

Dignum, V. Responsible Artificial Intelligence (Springer International Publishing, 2019).

Future of Life Institute. Open Letter on Autonomous Weapons (Future of Life Institute, 2015).

Future of Life Institute. Annual Report 2018. https://futureoflife.org/wp-content/uploads/2019/02/2018-Annual-Report.pdf?x51579

Montes, G. A. & Goertzel, B. Distributed, decentralized, and democratized artificial intelligence. Technol. Forecast. Soc. Change 141, 354–358 (2019).

Butler, A. J., Thomas, M. K. & Pintar, K. D. M. Systematic review of expert elicitation methods as a tool for source attribution of enteric illness. Foodborne Pathog. Dis. 12, 367–382 (2015).

Morgan, M. G. Use (and abuse) of expert elicitation in support of decision making for public policy. Proc. Natl Acad. Sci. USA 111, 7176–7184 (2014).

United Nations Human Rights. Sustainable Development Goals Related Human Rights (United Nations Human Rights, 2016).

Draft Committee. Universal Declaration of Human Rights (United Nations, 1948).