The role of artificial intelligence in achieving the Sustainable Development Goals

By Ricardo Vinuesa, Hossein Azizpour, Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna Felländer, S. Langhans, Max Tegmark, F. Nerini. Nature Communications, 2019

2,159 citations recorded by the source corpus.

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.

Subfields: Media Technology, Renewable Energy, Sustainability and the Environment, Safety Research

View Paper: https://www.semanticscholar.org/paper/3ce5a172a96008cbdc5ffedf4572b783301fd468

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Media Technology

1. Henrik Skaug Sætra
2. Shakir Mohamed
3. Hidemichi Fujii

In Renewable Energy, Sustainability and the Environment

1. Kun Yang
2. Liang Liu
3. Jun Liu
4. Seeram Ramakrishna
5. Guangtao Fu
6. Hidemichi Fujii

In Safety Research

1. William S. Isaac
2. Shakir Mohamed
3. Marie-Therese Png

Citations in the corpus

• Unraveling the hidden environmental impacts of AI solutions for environment (2021, positive): “Now that we have presented how the general framework of life cycle assessment can be adapted to AI solutions, we propose to use it for evaluating the complete benefits of an AI for Green service.”
• Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel (2021, background): “, 2020; Jung and Lim, 2020), and sustainable economic development (Vinuesa et al., 2020; Machado et al., 2020; Liu et al., 2021).”
• ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health (2023, background): “A science-driven debate to develop shared principles and legislation is necessary to shape a future in which AI has a positive impact on public health, not having such a conversation could result in a dangerous AI-fueled future (70).”
• Six Human-Centered Artificial Intelligence Grand Challenges (2023, neutral): “AI has been identified as a supporting technology in the achievement of 128 targets across all SDGs (Vinuesa et al., 2020).”
• Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century (2021, background): “Apart from the above-mentioned points, AI technologies are thought to have an effect on the achievement of [20] Sustainable Development Goals (SDGs) [21] and such a view requires us to revisit the use of AI technologies in different contexts including education.”
• Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research (2021, background): “system learned using AI can be applied for redirecting the energy resources in real-time so that the consumption of power can be reduced and efficiently utilized [363].”
• The Impact of 4IR Digital Technologies and Circular Thinking on the United Nations Sustainable Development Goals (2020, background): “In a recent study [46], these technologies had a 79% positive impact on the SDGs, whilst 35% showed a negative impact across all the goals.”
• A Framework for Evaluating and Disclosing the ESG Related Impacts of AI with the SDGs (2021, background): “A broad and nontechnical definition is beneficial for securing that all relevant impacts are accounted for when evaluating the impact of AI systems, and Vinuesa, Azizpour, Leite, Balaam, Dignum, Domisch, Felländer, Langhans, Tegmark and Nerini [17] (p.”
• Enhancing computational fluid dynamics with machine learning (2021, background): “In this sense, ML can be an enabler for more efficient operation of the high-performance-computing (HPC) facilities [7], or even for future computational architectures, including quantum computers [148], as shown in Table 1.”
• The role of deep learning in urban water management: A critical review. (2022, background): “…and practice in the water sector (IWA, 2019) and help tackle water challenges such as resource efficiency, water supply, water pollution, flooding and drought, contributing to achieving the water-related United Nations ’ sustainable development goals (Mehmood et al., 2020; Vinuesa et al., 2020).”
• Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, background): “humanitarianism” (Latonero 2019; Vinuesa et al. 2020), decolonial thinking shifts our view towards systems that instead promote active and engaged political community.”