research work on ai for sdgs

AI for social good: Improving lives and protecting the planet

Advances in AI have accelerated dramatically over the past two years, marked by an ability to design, train, and—most important—run AI at a large scale and for many millions of users. AI’s potential impact spans sectors  and could deliver positive social change.

About the authors

This report is a collaborative effort by Medha Bankhwal, Ankit Bisht , Michael Chui , Roger Roberts , and Ashley van Heteren , representing views from McKinsey Digital.

In fact, AI is already being used to further all 17 UN Sustainable Development Goals (SDGs)—from the goal of eliminating poverty to establishing sustainable cities and communities and providing quality education for all. Generative AI  has opened even more possibilities. In 2018, it was becoming clear that AI could play a major role globally in promoting not only productivity and economic growth but also social good. A report we published  at the time outlined how AI capabilities, from natural language processing to sound recognition and tracking, could be used in 170 use cases to benefit society 1 Additional research in 2023 yielded discovery of 13 more use cases piloted in 2018 that were not originally accounted for in our 2018 report , bringing the total to about 170. —including to promote equality and inclusion, improve crisis response, and protect the environment. 2 “‘ Tech for Good’: Using technology to smooth disruption and improve well-being ,” McKinsey Global Institute, May 15, 2019; Amine Aït-Si-Selmi, Eric Hazan, Hamza Khan, and Tunde Olanrewaju, “ Tech for Good: Helping the United Kingdom improve lives and livelihoods ,” McKinsey, July 31, 2020. Today, we have discovered a total of about 600 use cases, more than a threefold increase. 3 Our library of about 600 use cases is not comprehensive and will continue to evolve. This list of use cases per SDG should thus not be treated as exhaustive.

Methodology

We set out to understand AI’s potential impact on the UN Sustainable Development Goals (SDGs) and assess how private and charitable and public capital is flowing toward the SDGs with the highest potential for AI impact. We collected several kinds of data to do so:

Use case library and not-for-profit deployments. We created a library of 600 use cases in which AI could be used to advance the SDGs and more than 1,000 not-for-profit deployments of AI toward these goals. To build this data set, we used a range of publicly available sources made available by not-for-profits, foundations funding their activities, and the United Nations.

Survey and interviews. We surveyed about 60 experts from 17 countries and 48 organizations (including not-for-profits, foundations, technology companies, start-ups, academic institutions, and government) to learn their views related to SDGs where AI has the highest potential, AI risks, and barriers to scaling. Additionally, we interviewed more than 50 executives and senior leaders from not-for-profits, tech companies, and foundations globally.

Foundation grants. We analyzed more than 1,000 foundation grants filtered by the search terms “AI,” “ML,” “artificial intelligence,” and “machine learning” from 2018 to 2023. 1 Analysis focuses on grants from mostly US-based foundations, and there is typically a time lag since the data is based on IRS data. But the analysis is representative of distribution across SDGs as well as scale of funding. See “AI for Sustainable Development Goals,” Candid Foundation Directory, 2018–2023. Grants went to recipients in countries including Australia, Brazil, Belgium, Canada, China, India, Norway, Spain, Switzerland, the United Kingdom, and the United States.

Analysis of private capital investment. Based on data from PitchBook, we analyzed approximately 20,000 companies that have a clear focus on AI, machine learning, and big data and have gone through a funding round since 2020. Based on their descriptions, companies were tagged to one or more SDGs. The total lifetime funding of all tagged companies was used to determine the total funding per SDG. Because funding can be applicable to multiple SDGs, when the value of SDG funding is calculated, it exceeds the total amount invested when considered without SDG tagging. Roughly 35 percent of companies were headquartered in the United States.

In our 2024 report, we take another look at how AI can and has already become a key part of solutions to benefit people and the planet by mapping innovations and impact to the SDGs (see sidebar “Methodology”). The SDGs comprise 17 goals and 169 targets that aim to improve lives around the world and protect the planet. But the 2023 UN update on progress against the SDGs indicated that the world is on track to meet only 15 percent of SDG targets. 4 The Sustainable Development Goals report 2023: Special edition , United Nations, July 10, 2023. In real terms, this means that today, 2.2 billion people lack access to safe water and hygiene; 3.5 billion lack access to safely managed sanitation 5 “The 17 goals,” United Nations Department of Economic and Social Affairs, accessed April 24, 2024. ; roughly 3.3 billion live in environments highly vulnerable to climate change 6 “ Protecting people from a changing climate: The case for resilience ,” McKinsey, November 8, 2021. ; and 750 million are facing hunger. 7 “122 million more people pushed into hunger since 2019 due to multiple crises, reveals UN report,” World Health Organization, July 12, 2023.

AI’s potential across the SDGs and how funding for AI supports progress

While AI will affect all SDGs, experts we surveyed believe that AI has a particularly high potential to make a difference for five: Good Health and Well-Being (SDG 3), Quality Education (SDG 4), Climate Action (SDG 13), Affordable and Clean Energy (SDG 7), and Sustainable Cities and Communities (SDG 11). In fact, 60 percent of deployments of not-for-profit AI for social good were in these areas. Relative to their perceived AI-potential, the SDGs for Zero Hunger (SDG 2), Life on Land (SDG 15), and Peace, Justice and Strong Institutions (SDG 16) have many use case deployments, whereas Quality Education (SDG 4), Affordable and Clean Energy (SDG 7), and Climate Action (SDG 13) have fewer (Exhibit 1). We excluded Decent Work & Economic Growth (SDG 8); Industry, Innovation & Infrastructure (SDG 9); and Partnerships for Goals (SDG 17) from the not-for-profit deployment, foundation grants, and private capital analysis. This is because most projects can be tagged to these areas given the broad AI applicability. 8 In our analysis of 600 use cases, each use case was tagged to a single primary SDG and SDG target.

In analyzing where funding has been deployed toward harnessing AI for the SDGs, we see directional alignment with the experts’ opinion of the five highest-potential areas. The combined view of grant and private capital funding centers on Good Health and Well-Being (SDG 3), Quality Education (SDG 4), Affordable and Clean Energy (SDG 7), Sustainable Cities and Communities (SDG 11), and Climate Action (SDG 13). In addition, about 40 percent of private capital investments into the 20,000 AI companies analyzed contributed directly or indirectly toward at least one of the 17 SDG thematic areas. 9 SDGs 8, 9, and 17 were excluded in this analysis because of the general applicability of AI to those SDGs.

There are, however, exciting pockets of opportunity. Consider the relatively low private capital funding for Quality Education (SDG 4). Even for Affordable and Clean Energy (SDG 7) and Climate Action (SDG 13), more than 50 percent of the private capital investment went toward autonomous vehicles for improving energy efficiency and reducing emissions. This suggests further potential for private entities to deploy capital toward SDG themes where AI has high potential.

Geographic disparities in grant allocation remain high. An analysis of the location of grant recipients’ headquarters from a database of US-majority foundations reveals that, from 2018 to 2023, only 10 percent of grants allocated toward AI initiatives that address one or more of the SDGs went to organizations based in low or middle-income countries. 10 This analysis is not holistic, because it analyzed grants only from Candid, which focuses primarily on US-based foundations. Additionally, Candid’s database takes 1.5 to 2.0 years to update because of a lag from IRS data. See “AI for Sustainable Development Goals,” Candid Foundation Directory, 2018–2023. While organizations may have impact outside of the countries where they are headquartered, 60 percent of experts responding to our survey 11 We surveyed about 60 experts across 17 countries and 48 organizations. agreed that AI efforts today are not focused enough on benefiting lower-income countries (as opposed to higher income or developed countries), where the need and SDG impact can be the highest.

Challenges and risks of scaling AI for social good

Challenges in scaling AI for social-good initiatives are persistent and tough. Seventy-two percent of the respondents to our expert survey observed that most efforts to deploy AI for social good to date have focused on research and innovation rather than adoption and scaling. Fifty-five percent of grants for AI research and deployment across the SDGs are $250,000 or smaller, which is consistent with a focus on targeted research or smaller-scale deployment, rather than large-scale expansion. Aside from funding, the biggest barriers to scaling AI continue to be data availability, accessibility, and quality; AI talent availability and accessibility; organizational receptiveness; and change management. More on these topics can be found in the full report.

While overcoming these challenges, organizations should also be aware of strategies to address the range of risks, including inaccurate outputs, biases embedded in the underlying training data, the potential for large-scale misinformation, and malicious influence on politics and personal well-being. 12 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. As we have noted in multiple recent articles, 13 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024; “ The state of AI in 2023: Generative AI’s breakout year ,” McKinsey, August 1, 2023; New at McKinsey Blog , “ An inside look at how businesses are—or are not—managing AI risk ,” blog entry by Liz Grennan and Bryce Hall, August 31, 2023; “ What is generative AI? ,” McKinsey, April 2, 2024. AI tools and techniques can be misused, even if the tools were originally designed for social good. Experts identified the top risks as impaired fairness, malicious use, and privacy and security concerns, followed by explainability (Exhibit 2). 14 Our AI risks framework for social impact builds on McKinsey’s generative AI risks framework (see “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024). It includes additional categories such as political stability and environmental impact and excludes risks such as strategic risks that can be more relevant to for-profit enterprises. Respondents from not-for-profits expressed relatively more concern about misinformation, talent issues such as job displacement, and effects of AI on economic stability compared with their counterparts at for-profits, who were more often concerned with IP infringement.

Accelerating the deployment of AI for social good

Scientific breakthroughs have increased the effectiveness of AI at pattern recognition, prediction, and creation. This progress has coincided with a rapidly growing number of successful AI deployments, but, as we saw above, there are still challenges to scaling their use for addressing the SDGs. Realizing this potential will require stakeholders to collaborate more closely to ensure access to adequate talent and robust data solutions, as well as to AI applications and models that are more open-sourced or scalable across user geographies around the world, thereby meeting people at the point of need.

By collaborating to find ways to put AI to work at scale for social good, mission-driven organizations, governments, foundations, universities, ecosystems of developers, and businesses can help solve some of the world’s most challenging and intractable problems. They can help thwart human trafficking, ensure girls and children all over the world receive the education they deserve, protect forests from illegal deforestation, support the health and safety of pregnant women and newborns, and so much more. If these things aren’t worth fighting for, what is?

Medha Bankhwal is a consultant in McKinsey’s Bay Area office, where Michael Chui is a McKinsey Global Institute partner; Ankit Bisht is a partner in the Dubai office; Roger Roberts is a partner in the Silicon Valley office; and Ashley van Heteren is a partner in the Amsterdam office.

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‘Tech for Good’: Using technology to smooth disruption and improve well-being

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A framework for evaluating and disclosing the esg related impacts of ai with the sdgs.

1. Introduction

2. sustainability, the sdgs, and efforts to tackle the sustainability of corporations.

There are several reasons for the current concern with corporate social responsibility. In recent years, the level of criticism of the business system has risen sharply. Not only has the performance of business been called into question, but so too have the power and privilege associated with large corporations. Some critics have even questioned the corporate system’s ability to cope with future problems. [ 14 ] (p. 59)

3. The Sustainability Impacts of AI

… we considered as AI any software technology with at least one of the following capabilities: perception—including audio, visual, textual, and tactile (e.g., face recognition), decision-making (e.g., medical diagnosis systems), prediction (e.g., weather forecast), automatic knowledge extraction and pattern recognition from data (e.g., discovery of fake news circles in social media), interactive communication (e.g., social robots or chat bots), and logical reasoning (e.g., theory development from premises). This view encompasses a large variety of subfields, including machine learning.

Sustainability reporting, as promoted by the GRI Standards, is an organisation’s practice of reporting publicly on its economic, environmental, and/or social impacts, and hence its contributions—positive or negative—towards the goal of sustainable development. [ 38 ] (p. 3)

4. Using the SDGs to Evaluate AI System Impacts

4.1. the process and the framework, 4.2. ai evaluation in practice.

And, as we make advancements in AI, we are asking ourselves tough questions—like not only what computers can do, but what should they do. That’s why we are investing in tools for detecting and addressing bias in AI systems and advocating for thoughtful government regulation. [ 60 ] (p. 12)

… is a powerful force for good, and all of us here at Microsoft are working together to foster a sustainable future where everyone has access to the benefits it provides and the opportunities it creates. [ 42 ] (p. 4)

5. Conclusions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Sætra, H.S. A Framework for Evaluating and Disclosing the ESG Related Impacts of AI with the SDGs. Sustainability 2021 , 13 , 8503. https://doi.org/10.3390/su13158503

Sætra HS. A Framework for Evaluating and Disclosing the ESG Related Impacts of AI with the SDGs. Sustainability . 2021; 13(15):8503. https://doi.org/10.3390/su13158503

Sætra, Henrik Skaug. 2021. "A Framework for Evaluating and Disclosing the ESG Related Impacts of AI with the SDGs" Sustainability 13, no. 15: 8503. https://doi.org/10.3390/su13158503

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Artificial Intelligence for Advanced Sustainable Development Goals: A 360-Degree Approach

• First Online: 11 June 2024

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• Rahul Joshi 4 ,
• Krishna Pandey   ORCID: orcid.org/0000-0001-8290-3681 4 &
• Suman Kumari   ORCID: orcid.org/0000-0002-4691-4065 4  

Part of the book series: World Sustainability Series ((WSUSE))

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The phenomenon referred to by the phrase “artificial intelligence” has the potential to have both positive and negative impacts on individuals and the environment, and there is now a substantial amount of attention put on the research and assessment of these effects. The United Nations 2015 developed a set of seventeen Sustainable Development Goals (SDGs), which included social, economic, and environmental objectives. This chapter introduces two theoretical models that may be used to determine how Artificial intelligence fits in with overall objectives. It argues that the current concentration on AI applications risks leading to disappointment, even as AI, in every sense, is likely to play a crucial role in efforts to accomplish the SDGs. First, it frames the issue within the larger one of aligning international Research & Development activities with the SDGs. This chapter focuses on presenting and discussing up-to-date information on the degrees of alignments and disarray and the potential for alternate configurations to create more precise routes. These are starting to appear from the food business to the energy industry, but AI and the digital world have some catching up to do. Second, it shows how different forms of AI may play different helpful and required roles in achieving Sustainable Development Goals in different contexts, such as cities, regions, and nations.

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Joshi, R., Pandey, K., Kumari, S. (2024). Artificial Intelligence for Advanced Sustainable Development Goals: A 360-Degree Approach. In: Prabhakar, P.K., Leal Filho, W. (eds) Preserving Health, Preserving Earth. World Sustainability Series. Springer, Cham. https://doi.org/10.1007/978-3-031-60545-1_16

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Artificial Intelligence

Artificial Intelligence (AI) has emerged as a powerful tool in advancing the Sustainable Development Goals (SDGs), a universal call to action to end poverty, protect the planet, and ensure that all people enjoy peace and prosperity by 2030. AI's capabilities, ranging from data analysis to predictive modeling, offer unprecedented opportunities to address complex global challenges outlined in the SDGs. For instance, AI can analyze vast datasets to identify trends and insights related to poverty, hunger, health, education, and environmental degradation, enabling targeted interventions and resource allocation. In the realm of healthcare, AI-powered tools and systems are revolutionizing diagnostics, treatment planning, and patient monitoring, contributing significantly to SDG 3, which aims to ensure healthy lives and promote well-being for all at all ages. By analyzing medical data, AI can identify disease patterns, predict outbreaks, and suggest preventive measures, thereby enhancing healthcare accessibility and affordability.

Moreover, AI's role in climate action (SDG 13) is another area where its impact is profound. Through the analysis of climate data, AI helps in predicting weather patterns, assessing the impact of climate change, and optimizing the use of renewable energy sources. This aids in developing more effective strategies for disaster risk reduction, sustainable agriculture, and energy consumption, aligning with the goals to combat climate change and its impacts. Additionally, AI contributes to achieving SDG 4 (quality education) by personalizing learning experiences through adaptive learning platforms that cater to the individual needs of students, thus improving access to quality education globally. However, the integration of AI into SDG efforts also presents challenges, including ethical considerations, data privacy concerns, and the digital divide. Ensuring that AI technologies are inclusive, transparent, and aligned with human rights is crucial to leveraging AI for the global good without exacerbating inequalities or undermining privacy and security.

The relationship between AI and the SDGs is symbiotic; while AI offers tools and solutions to accelerate progress towards the SDGs, the pursuit of these goals provides a framework for developing and implementing AI technologies responsibly and ethically. As we harness AI's potential, it is imperative to adopt a multi-stakeholder approach that includes governments, the private sector, academia, and civil society. This collaborative effort is essential to create policies, standards, and practices that ensure AI's benefits are widely distributed and its challenges are effectively managed. By doing so, we can maximize AI's contribution to sustainable development, creating a more equitable, resilient, and prosperous world for all.

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• 1 No poverty
• 2 Zero hunger
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• 6 Clean water and sanitation
• 7 Affordable and clean energy
• 8 Decent work and economic growth
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OSDG Initiative Recognized in Top 100 AI Projects for Advancing Sustainable Development Goals

November 29, 2023.

As a free, open-source tool developed by PPMI and SDG AI Lab, OSDG can swiftly review uploaded texts, identifying its relevancy to the 17 SDGs. This tool enables users to conduct comprehensive SDGs analyses and visually explore data from thousands of documents in 17 languages.

New York - The OSDG initiative, a collaborative effort between the United Nations Development Programme (UNDP) and European research and policy analysis centre PPMI, has been honored as one of the IRCAI  Top 100 Artificial Intelligence (AI) initiatives driving progress toward the Sustainable Development Goals (SDGs). This prestigious recognition is annually bestowed by the International Research Centre on Artificial Intelligence (IRCAI) under the auspices of UNESCO. 

OSDG represents a unique partnership, supported by a global volunteer community. It encompasses an innovative online tool for SDG analysis and a curated dataset for Machine Learning applications. This recognition underscores OSDG's remarkable contribution to the SDGs by uniting innovative AI solutions with a dedicated volunteer community. 

Initially conceived as a basic online text analysis tool for SDG monitoring, OSDG has rapidly evolved into a sophisticated resource for users around the world. As a free, open-source tool developed by PPMI and SDG AI Lab, OSDG can swiftly review uploaded texts, identifying its relevancy to the 17 SDGs. This tool enables users to conduct comprehensive SDGs analyses and visually explore data from thousands of documents in 17 languages.

Moreover, the OSDG team has curated a one-of-a-kind  OSDG Community Dataset , containing 42,065 text excerpts. These have been rigorously vetted by 2,600 dedicated volunteers, ensuring perfect alignment with the SDGs. This dataset, downloaded over 4,000 times, has significantly contributed to several prominent academic publications. The community of online volunteers, which includes almost  1000 Online UN Volunteers from over 50 countries, have contributed in various fields like science, technology, healthcare, and public policy in the realm of sustainable development.

The versatility of OSDG extends to academic and practical applications. Prominent universities, including University College London, University of Hong Kong, Hohenheim University, Nazarbayev University and York University, have used OSDG to align their curricula and research with the SDGs. Both dataset and tool have also featured in publications covering topics ranging from the European Green Deal to urban resilience, computational thinking, carbon emissions, and technical papers on knowledge graphs and the RoBERTa approach. 

In government policy-making and national planning, OSDG has proven to be an invaluable asset. For example, the Government of Turkiye, in collaboration with the SDG AI Lab, used the OSDG tool to align the 11th Development Plan with the SDGs. This process not only identified frequently addressed goals but also highlighted less targeted ones, offering a template for nations worldwide to integrate technology for more informed policy making. 

The success of the OSDG has sparked the development of new tools and research initiatives. The UNDP global team, for instance, has launched the SDGs Push platform, powered by the OSDG Community Dataset. Similarly, the dataset has been instrumental in developing the GIZ Policy Action Tracker and has been utilized by the policy analysis startup Overton. The OSDG project sparks SDG-related innovations and global cooperation. 

The recognition of OSDG among the Top 100 initiatives advancing the SDGs underscores the critical importance of digital technologies and volunteerism to achieve sustainable development. The project's global impact supports various stakeholders, including academia, researchers and government, in their pursuit of the SDGs.

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In this work we carry out the first study on the impact of AI on the possible achievement of the 17 UN Sustainable Development Goals. Potential benefits were identified on 134 of the 169 targets, however 59 targets may be inhibited by AI.

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The role of artificial intelligence in achieving the Sustainable Development Goals - Nature Communications

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.

Artificial intelligence (AI) is changing everybody’s lives in a number of different ways. However, despite the fact that it is progressively being used in more applications, there seems to be a fundamental lack of understanding from the public, policy-makers and even AI developers regarding its capabilities, potential risks and benefits. Moreover, there is not any comprehensive and systematic assessment of the potential (present and future) impact of AI on our society and our planet. Driven by this important gap in the literature, we decided to conduct a thorough investigation to assess the published evidence of positive and negative impacts of AI, and we framed such impacts within the 17 Sustainable Development Goals (SDGs) of the United Nations (UN). These SDGs are subdivided into a total of 169 targets, which define the areas we should be focusing on to ensure a sustainable future, and include Goals ranging from eradicating poverty, to ensuring quality education and equality worldwide, as well as enabling cleaner and safer cities and fighting climate change. 

The 17 SDGs span a wide range of knowledge areas, and therefore we decided to put together a group of experts in very diverse topics such as engineering, sustainability, ethics, biology, and of course AI. The study was initiated at KTH Royal Institute of Technology in Stockholm (Sweden), and we involved a total of 10 researchers from all over the world (including the United States, New Zealand and several locations in Europe) in order to provide a comprehensive and unbiased assessment of the literature. We first collected a vast database of studies showing either positive or negative impacts of AI on each of the 169 targets, and then we organized a number of workshops to evaluate each other’s work, discuss, and converge towards a unified view. This process was extremely rewarding and stimulating due to the diverse perspectives from the authors. The main conclusion of the work is that AI can positively contribute to the achievement of 134 targets across all the SDGs, whereas it can inhibit 59 targets. One of the most challenging aspects of this work was to decide what can be considered as “relevant evidence” to establish a connection between AI and a particular target. Indeed, we established different criteria to categorize the types of evidence, based on their appropriateness to establish the connection. 

Our research finds that AI can help towards the achievement of all the SDGs, generally through a technological improvement that helps to overcome a certain existing barrier. For instance, AI can help to develop more accurate and robust methods to measure pollution levels in urban areas, thus allowing to develop more comprehensive plans to improve their air quality. Also, satellite images can be analyzed through AI to track and identify areas of growing poverty, with the aim of developing coordinated actions to provide better help. On the other hand, some of the main drawbacks lie in the need for large data centers to perform the required calculations to obtain these results, and the lack of access to such facilities could eventually lead to a net increase in inequalities. Furthermore, we identify that there is a lack of proper regulation towards AI development, mainly due to two reasons: the speed at which this technology is growing, and the general lack of insight into AI. Therefore, we believe that it will be essential to initiate a global debate aimed at developing shared principles and legislation among nations and cultures regarding AI. This will be the only way to ensure a sustainable, AI-facilitated future. 

More information here:  https://www.nature.com/articles/s41467-019-14108-y

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Harnessing Artificial Intelligence for Sustainable Development Goals (SDGs)

Remarks by the UN Deputy Secretary-General at the Opening of the ECOSOC Special Meeting

Excellencies, Ladies and gentlemen,

I thank Ambassador Narváez, President of the Economic and Social Council, for inviting me to part of this important event.

Our world is grappling with complex challenges — from conflict and climate change to chronic poverty and rising inequalities.

We all know that the 2030 Agenda towards a more equitable, resilient and sustainable future is woefully off track.

Today’s topic on how to harness Artificial Intelligence for SDGs cannot be timelier. Applied safely, it can accelerate progress towards the SDGs, enhance decision-making, and drive innovation. It can and it must.

This is not a future dream but today’s reality.

AI is already optimizing energy use, improving medical diagnostics, monitoring biodiversity, expanding educational opportunities – and so much more.

Many of you are seeing the benefits of AI across all sectors in your countries.

Yet these technologies also pose grave risks. They can displace jobs, exploit gaps in global governance, and exacerbate bias, discrimination, and misinformation. And they can do so on a monumental scale.

Excellencies,

Our task is to harness this powerful technology to accelerate sustainable development, while mitigating its harms.

This means accountability for those who create AI systems and for those that use them. The power, speed, and impact of AI is truly global, and accountability must be part of the package.

To do this, we must ensure that AI is effectively governed, that it is equitable, accessible, and ethical.

First, on equity. It is essential that we have an equitable access to AI tools, applications and infrastructure – including quality data and computational resources.

Equity also requires capacity development and technology transfer. We cannot allow all the benefits and opportunities of AI to be concentrated in the hands of those who are already way ahead.

We cannot allow the digital divide to widen.  

Second, it is essential that AI is accessible.

That means that skills are developed through education and lifelong learning – particularly for vulnerable groups to ensure that they are not pushed further behind.

It means individuals and societies using this technology to advance employability and seize the opportunities of new jobs that will be created by AI rather than passively allowing AI to replace jobs.

And it means adopting collaborative approaches that align responsible development with solutions that would respect diversity of cultures, while promoting locally generated data. The “Data commons” is one example.

To help us get there, Governments must invest in digital infrastructure, support new business opportunities, and leverage the data that they have for economic use in their countries. This should go hand in hand with training for government officials and re-skilling programs for the broader work force.

Third, it is imperative that AI is ethical and transparent. We cannot simply take current AI models and data sets, which have bias and discrimination baked into them, and hope that they will rescue the SDGs.

We need inclusivity in data that underpins these models, and diversity among the people that are building them.

Fourth, we need effective governance. This requires a shared sense of responsibility.

I welcome the urgency and ambition of the zero draft of the Global Digital Compact. What we need for this Compact is more ambition, not less, and we need Member States to adopt a global perspective, beyond narrow national interests.

I am encouraged by the potential of the high-level AI Advisory Body and the Global Digital Compact, as well as by the AI safety initiatives that are been developed at regional, national, and community levels by our agencies.

The Global Digital Compact must accelerate the application of ethical and diverse AI tools to solve the challenges facing the SDGs.

I urge Member States to be ambitious in these negotiations. And to engage fully and actively in global conversations on AI governance, while advancing a common understanding.

It will take a concerted and coordinated approach from governments, businesses, civil society, international institutions to develop responsible, ethical and inclusive AI governance.

We must ensure that standards are common, that regulation is harmonized, and that human rights are at the core.

As we look to September’s Summit of the Future here in New York, we must recognize that an open, safe, and secure digital future can only be achieved through international cooperation.

What we are seeing with AI is only the beginning of more technological advancements.

Developments in quantum computing, biotechnology, synthetic biology and advanced robotics will continue to transform our societies in a significant way.

We need a global conversation on the best way to leverage these technologies to advance sustainable development for all.  

Let us get the SDGs back on track and close the digital divide by safely harnessing technology opportunities for those who need them most.

I urge Member States to make the most of this opportunity and make that vision a reality in people’s lives.

Together, let’s build a world where AI and other technologies will serve the entire humanity and leave no one behind.

AI for Good blog

How ‘responsible ai’ can boost sustainable development.

• Inclusivity
• Innovation & Creativity
• 17 April 2019

The potential value from artificial intelligence (AI) is enormous:  some $16 trillion by 2030 , according to a PwC study. But what are the costs of AI when not done properly — and what are some of the risks?

What does it really mean to be doing AI “responsibly”? Can you really do  Responsible AI  without worrying about the social consequences? How can you apply the principles of responsible AI more broadly toward achieving sustainable development goals?

AI is being applied in most industry sectors — ranging from agriculture to aerospace — and across functional areas, from strategy to support.

Increasingly, countries in varying stages of economic advancement are making plans to apply AI too.

While this can increase the profits of companies and gross domestic product (GDP) of countries in the short term, if not done responsibly, it could help create greater inequity within each country, greater inequity among countries, increased use and depletion of natural resources to fuel the AI-led growth of economies, further decreases in biodiversity and ill treatment of other species, and adverse effects on the climate.

A ‘holistic discipline’

PwC’s Responsible AI is a holistic discipline: It is not just about what you build, but why and how you build it — as well as the long-term implications of the use of AI for your customers, staff, and society at large. It is not just about the technology itself. It is about the governance of AI, its impact on people, and the process of designing, building, and maintaining it.

The overarching principles that govern these dimensions are rooted in the society’s ethics and values. The governance of AI and algorithms — especially the monetary value AI brings and the accompanying risks that need to be mitigated — are Board and executive management decisions.

RELATED: How will AI accelerate sustainable development? Find out at the AI for Good Global Summit.

The process of designing, building, running, and maintaining AI should be embedded within the broader context of how a company operates. In addition to all these, it is about how PwC’s AI models are built — specifically, addressing issues such as fairness, transparency, interpretability, explainability, safety, security, ethics, values, and accountability.

Addressing the SDGs

Responsible AI addresses four of the United Nations (UN)’s seventeen  Sustainable Development Goals , namely gender equality, decent work and economic growth for all, industry innovation and infrastructure, and generally reducing societal inequality.

Primarily, responsible AI is concerned about fairness and equality of gender, race, or similar protected attributes.

Acting responsibly in the corporate context may or may not (depending on the purpose and stated vision of the company) take into account the broader topics of human rights, the well-being of humanity and other species, and protecting and nurturing our planet’s biodiversity and natural resources. In other words, responsible AI in the corporate context takes into account some of the people- and policy-related  goals , while not always addressing those related to the planet and human condition.

‘Fourth Social Revolution’?

As has been argued at the World Economic Forum, the Fourth Industrial Revolution should be accompanied by the  Fourth Social Revolution .

Individuals, corporate entities, nations, and other supra-national bodies should include metrics that are broader than revenues and profits.

Some or all objectives outlined in the SDGs should be part of a corporate socially responsible vision, plan, and metrics. For example, global corporations that rely heavily on air travel should commit to becoming carbon neutral; employees who travel regularly should be given data not just on the miles that they have flown, but the CO2 emissions created as a result of their travel.

Employees and corporations together can work toward offsetting emissions through initiatives to plant more trees. Air travel booking sites could feature the  Carbon Emissions Calculator from ICAO , for example, and, based on the CO2 emissions, there could be a link to an environmental organisation such as  Arbor Day  or  Carbonfund  to offset the emissions by planting more trees.

As outlined in a World Economic Forum  Harnessing Artificial Intelligence for the Earth  report developed in partnership with PwC and the Stanford Woods Institute for the Environment, AI can play a pivotal role in addressing six key areas — specifically climate change, biodiversity and conservation, healthy oceans, water security, clean air, and weather and disaster resilience. But these AI use cases should not be looked at as isolated programs to address the effects of economic development, but should instead be addressed holistically to help get to the root causes impacting the planet, human rights, and human well-being.

Organizations that claim to apply AI in a socially responsible manner should incorporate not just attributes like fairness, accountability, safety, and transparency, but also take into account additional factors, such as AI’s impact on jobs, the human condition, biodiversity, energy, climate, and so on. The additional criteria will vary depending on what products and services a given company offers, the impact of these factors on the environment, and what AI algorithms the company is using in the creation of these products and services.

PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see www.pwc.com/structure for further details. Views expressed in this article do not necessarily represent the views of ITU.

Navigating AI’s Legal and Ethical Frontiers

Generative AI’s Transformative Potential

Brain Machine Interface Press Conference at AI for Good Global Summit 2024

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Methods for Extremely Rapid Observation of Nutritional Status(MERON)

Kimetrica has developed an application called MERON. MERON is a machine learning tool - Method for Extremely Rapid Observation of Nutritional Status to detect malnutrition using photographs. It allows for a non-invasive, time efficient, and tamper-proof approach to assessing the malnutrition status of an individual by using a facial recognition and processing algorithm.

Published in 2017

Artificial Intelligence and Data Analytics for Human Development

This article explains the essentials of AI and data-driven analytics, analyzes the solution space of a number of human development problems, and identifies the most valuable contributions that AI and data analytics can make.

Published in 2018

Keeping people safe with AI-enabled flood forecasting

The flood forecasting pilot initiative incorporates AI and physics-based modeling to provide accurate real-time flood forecasting and alerts. The models can more accurately predict not only when and where a flood might occur, but also the severity of the event.

Published in 2019

Technology for poverty

iFLYTEK continues to use AI technology to help poverty areas solve educational and medical difficulties.

Combining satellite imagery and machine learning to predict poverty

The study used machine learning to assess consumer spending and asset wealth, using high-resolution satellite imagery to measure poverty in Africa.

Published in 2016

AI-powered Urban Development Architect

The project aims to create a matching algorithm for refugees by modeling the ideal refugee migration patterns and recommend appropriate destination countries. In this way, the social and economic pressures in those countries could be alleviated, and the disruption to the lives of migrants and local inhabitants could also be minimized.

AI-based financial advisor for low-wage workers

The financial advisor system can learn from historical data to predict the account balances of individuals for a future time period, analyze the spending patterns of users, and provide key insights into their financial health. With its help, low-income individuals and families can better plan their financial spending behavior and avoid a vicious cycle of debt and poor credit.

DYMAXION LABS

Dymaxion Labs facilitates our understanding of the physical world by automating the analysis of satellite-SAR-aerial-drone imagery, climate data, public market data, proprietary data models, and real-time Internet of Things (IoT) sensors.

Layers against inequality

Paraguay has a broad rural social group with modest means and reduced access to financial leverage to help them get out of that situation through loans or technical assistance. Furthermore, it is common for these individuals to be registered in a credit bureau or identified with a high financial risk which prevents them from accessing new financing to make investments.

ParaEmpleo is the first digital platform in the Latin American market that can make a job match through the use of algorithms free from discrimination in real time and available to any player in the job market without compromising the privacy or routine operating mode of users. It is unrestricted, free of charge and available in a mobile version.

Vertical Farm

With sensors, cameras, artificial intelligence, etc., Plenty provides the optimum conditions for crops to grow and uses robots for automated cultivation in Vertical Farm.

AI-BASED EARLY PEST WARNING SYSTEM

Identify pests using smartphone app.

Global Fishing Watch

Monitoring global fishing activity

A.I. powered precision agriculture

Orbem.ai is as an interdisciplinary and international team from the Technical University of Munich. Their A.I. powered digital technology automatically analyzes and classifies biological samples, revolutionizing the way we produce food to meet the needs of a rising population.

Prediction model for crop productivity in India

This project is using climate forecasts of Global Climate Models to predicted crop-yields in near-future in India.

AI-driven credit risk scoring system to farmers

Harvesting is building Agriculture Intelligence Engine to help financial institutions make and monitor loans to farmers in the developing world. There are 3 products in Harvesting Farmer Network: Credit Risk Solution, Farm Land Monitoring, and Land Record Monitoring.

Jaguza Livestock Application

Jaguza helps people solve their farming problems, concerning disease, Market, Farming Methods, information sharing, Livestock Monitoring, social security and Reporting. The Livestock Farmers use drones to obtain an aerial overview of the area in which they keep their livestock. They can track and monitor their livestock remotely, identifying any issues in real time.

Aerobotics uses drone imagery and artificial intelligence to monitor crops and warn about potential risks, early pest, and disease detection.

Transforming the way food is grown with Data and AI

Prospera’s technology collects, digitizes & analyzes vast amounts of data to help growers control & optimize their production and growing systems. Prospera’s system provides growers easy-to- use digital tools to achieve better yields, healthier crops, and higher profits.

Child Growth Monitor

This is a mobile app using augmented reality in combination with AI. By determining weight and height through a 3D scan of children, the app can instantly detect malnutrition.

Using AI to advance the health of people and communities around the world

This system captures a high-resolution image of the back of the patient’s eye that clinicians use to identify diabetic retinopathy and determine if the patient is at risk of vision damage.

Machine Learning for Rapid Identification of HIV-related Social Media Data

Using an existing social media dataset that was associated with HIV and coded by an HIV domain expert, they tested whether four commonly used machine learning methods could learn the patterns associated with HIV risk behavior. They used the 10-fold cross validation method to examine the speed and accuracy of these models in applying that knowledge to detect HIV content in social media data.

The technology uses AI as an adjunct to clinician interpretation of electronic fetal heart rate monitoring.

Watson Health

IBM Watson Health was created to help solve some of the world's most pressing health challenges through data, analytics and AI.

The impact of artificial intelligence in the diagnosis and management of glaucoma

Use AI to examine and treat glaucoma patients

Expanding the Application of Deep Learning to Electronic Health Records

AI helps the clinical care team provide the best care for patients.

Similar-Image Search Tool for Pathology

The study explored different modes of refinement for image-based search, and evaluated their effects on doctor interaction with SMILY.

Protecting Patients from Medication Errors

The study would identify prescriptions that looked abnormal for the patient and their current situation,preventing medication errors in patients

Do-it-yourself artificial intelligence

The AIY Vision Kit from Google lets you build your own intelligent systems that can see, speak, and recognize objects using machine learning. Everything you need is provided in the kit.

Hujiang makes use of AI technologies to improve learners' learning efficiency.

Hujiang proposed AI technologies based systems to enhance learners' learning efficiency.

China tries to correct essays with AI for its students.

China started to work with 60,000 schools for automatic essay correction, with a level of precision matching humans in 92% of the cases.

Plan Ceibal proposed an online adaptive learning solution--Mathematics Adaptive Platform.

Plan Ceibal in Uruguay introduced Mathematics Adaptive Platform, which has been adapted to the national curriculum. It is a tool that provides personalized feedback according to each student's skill level based on an analysis of student experiences.

Brazil created an AI equipt platform -- Mec Flix .

In Brazil, the federal government created Mec Flix as a state educational platform. It is a video content platform designed to prepare students for the national higher education examination.

IBM proposed ’Simpler Voice: Overcoming Illiteracy’ project.

IBM introduced SimplerVoice: a key message and visual description generator system to help low-literate adults navigate the information-dense world with confidence on their own.

Xiaoyuan Kousuan checks math homework to help parents, teachers

Xiaoyuan Kousuan had gained increasing popularity in China since its launch a year ago. It claims to have checked an average of 70 million arithmetic problems per day, saving users around 40,000 hours in total.

Knewton puts achievement within reach for everyone with adaptive technologies and products that deliver personalized and lasting learning experiences.

Knewton provides adaptive learning technology to bring customized education to people all around the world. Education companies, schools, and teachers all use Knewton to run their digital course media and tailor materials to students' needs. The platform helps improves pass rates, motivates students, and helps them get better exam scores.

mindojo--making quality education universally accessible and affordable

Mindojo is an intelligent learning platform. It's simple, you map out the content of your course, and Mindojo creates a chat-like learning process, personalized for each of your students. As students learn, Mindojo both adapts better to students and improves your content.

AI helps research discovery

Iris.ai uses Natural Language Processing technologies to review massive collections of research papers or patents to find the right documents, extract all their critical data, or identify the most precise pieces of knowledge.

AI for culture heritage-Women who changed science

This project highlights the life stories of women scientists who won the Noble Prize, and discovers the deep connections of these women.

IoT based Artificial Intelligence Women Protection Device

This project describes and provides a smart intelligence security device to protect India women from rape.

Gender Gap Grader

The project provides APIs to help companies and organizations to measure the gender gap by big data analysis.

The Tech She Can Charter

The group the organizations take action to help women in tech careers, including the AI domain.

Millennia2025 MAD-Skills Intelligence Platform

The project mainly provides digital tools for women to learn and enhance working techniques.

Women Techmakers

Google's Women Techmakers program provides visibility, community, and resources for women in technology.

Algorithmic Justice League

The organization is founded by a famous female scientist, Joy Buolamwini, to develop existed algorithms to overcome the large racial and gender bias, especially facial recognition technologies.

WeAllFit Scholarships for Women in Artificial Intelligence

The project aims to provide 100% founded scholorship for women in AI AI Engineer Bootcamp.

Generation AI

This project uses artificial intelligence to help realize and uphold child rights.

HelpSelf Legal

This project provides a fee-based website to help domestic violence survivor to file the legal paperwork for a restraining order against an abuser.

WINT - Water Intelligence

WINT uses artificial intelligence to detect water waste and leaks in real-time and can alert the user about the leakage of water and shut it down automatically at the source with higher precision compared to staff. It not only conserves water resources but also prevents damages caused by water leaks in commercial and government buildings and infrastructures.

Garbage sorting automation

Garbage sorting automation is one of the Baidu Star Plan projects. The garbage classification automatic scheme created by Baidu flying paddle ability and model was trained by using the object detection and image segmentation model of flying paddle.

CleanRobotics

CleanRobotics is dedicated to Creating sustainable, innovative, tech-driven solutions to persistent environmental problems. It is opening up a new program to replace all of your facilities waste stations with TrashBots for free.

Remix Water

RemixWater is one of Hitachi's products. It is an integrated system of seawater desalination and water reuse using artificial intelligence technology to develop detailed indicators. The system aims to save energy and costs by mixing seawater and wastewater.

ConserWater

ConserWater is a platform using satellite data and AI to help farmers reduce water wastage and predict exactly how much water farmers need to give to their crops at any time. It also includes AI-based irrigation recommendations, plant disease detection, irrigation leak detection, farm-specific weather, and so on.

RUBSEE European Project

The RUBSEE project is a real time monitoring system using AI and computer vision to follow waste management processes in waste treatment plants. It aims to optimize the waste industry s environment and improve its economic and regulatory compliance.

EQWATER：Intelligent Water Supply Network Monitoring and Control for EQuitable Distribution of WATER within a Mega city

EQwater is a project using data analytics and machine learning to identify inequities in water distribution and develop data-based recommendations to resolve them.

Forecasting regional-scale water shortages

This project will assimilate data from disparate sources, using Microsoft Azure resources to host and deploy machine learning models, then create a dashboard that people can interact with on a server. It will provide regulators, advocacy groups, and the public with scientifically informed estimates of the impact of extreme drought events on drinking water availability.

BasinScout Platform

BasinScout Platform uses satellite data and machine learning to help identify the best places to improve groundwater and surface water quantity and quality， and address pressing watershed management needs.

They are coming—for your trash. Sorting through 67 million tons of glass, plastic and paper is dirty, low-paid, mind-numbing work. Matanya Horowitz’s AMP Robotics wants to take humans off the job.

At an RDS of Virginia recycling center in Roanoke, two spider-like, 300-pound robots sort through an unending line of trash. One robot’s skinny leg, which relies on computer vision to detect recyclables, plucks a hunk of blue plastic off a conveyor belt, while the other’s grabs a piece of an old water bottle. The machine then places those bits into sorting bins using a vacuum gripper.

Published in 2020

AI for energy storage, maintenance and delivery

From machine learning to computer vision, deep learning to virtual assistants and autonomous vehicles to robotics, Shell has been focused on a range of technologies that have supported advances in AI. Including exploration production and maintenance with less emission, and EV cars recharging with lower costs.

AI for the management and operation of solar power plants.

The company has launched an AI-based diagnostic and optimization solution that captures and analyzes all the production data of a solar power plant, and daily management is optimized and guided.

Published in 2015

Machine learning techniques for energy forecasting

Nnergix company uses both satellite data and machine learning (ML) algorithms for more accurate prediction of energy production. The satellite data is generated from satellite images and they are are prepared for large-scale and small-scale weather models.

Published in 2013

UK Power Networks Launch Active Newtorks Management Scheme

ANM tracks how the grid interacts with different energy loads to adjust its components and to achieve efficiency gains. When a new energy producer (such as a solar plant) starts working, or new energy consumers begin to connect to the grid, the ANM adjusts the grid accordingly. Therefore, ANM also lays the foundation for electric vehicles to be charged using the smart grid.

AI in energy security

SparkCognition combines parsimony, sensors and data generated from operations to predict when critical infrastructure will collapse.

Tech companies are destructing climate by providing AI tools to oil and gas industry

Tech giants such as Google, Amazon and Microsoft are providing cloud computing services and AI support for oil and gas companies. It not only accelerates the location and extraction of fossil fuels but also makes them appear on the market faster and cheaper. According to GreenPeace reports, these technology companies have contradicted with their commitments to address climate change.

Samsung Brightics AI

Brightics AI is a prescriptive algorithm and Artificial Intelligence (AI)-based analysis platform. The platform can be applied to the various sectors ranging from manufacturing, marketing, logistics, security, IoT, health care and etc.

KT MEG e-Brain

E-brain is a Korea Telecom’s (KT) artificial intelligence-powered energy analytics platform. Capable of machine learning, KT’s e-Brain is able to factor weather, usage patterns and profitability to generate its own prediction models that enable efficient management of energy generation, energy consumption, energy trading and vehicle charging.

Ennet Eye (powered by EnergyLink)

ENNET Corporation, an energy company, is collaborating with COzero, a venture company in Australia, to create an energy management system for corporations, that utilizes artificial intelligence. The system analyzes energy use throughout the day and provides timely notifications and advice on how to reduce it. The system is based on COzero’s EnergyLink platform and has been adapted to the Japanese market. It is offered as a paid service to ENNET’s customers.

ENIT Systems is a German start-up that offers an intelligent solution to save energy. With the ENIT Agent, the company offers a full-fletched energy solution for monitoring electricity, heat, gas, steam or water consumption in companies. By integrating data logger, gateway, database, server and analysis software in one device, the ENIT Agent is a new concept in regards to energy management.

Salesforce Einstein AI

Einstein AI, proposed by SalesForce, is an artificial intelligence system embedded inside the SalesForce CRM system that can help small and medium-sized businesses to explore and analyze customer data and to focus on marketing. It integrates multiple AI technologies to extract customer preference from their phone conversations, emails and even social media posts so that the salespersons could comm

MonkeyLearn

MonkeyLearn is a platform which provides tools that allow users to train artificial intelligence agents within a few hours without the need for coding ability or expertise in machine learning. Instead of hiring data scientists and market experts, small enterprises can easily incorporate AI in their business by merely feeding the customer data into the easy-to-use interface provided by MonkeyLearn.

Crayon is an AI-powered analysis tool which helps small enterprises to keep track of the digital movements of their competitors from hundreds of millions of sources. It analyzes the strengths and weaknesses of other companies by monitoring and investigating the price fluctuation, the market campaigns, customer feedback, etc. The users could adjust their strategies according to the market trends di

Abu Dhabi National Oil Company and IBM

In the case of this service, Abu Dhabi National Oil Company (ADNOC) and IBM co-created a recognize software for classifying the characteristics of rock samples. This software is a faster and more accurate tool to classify rock samples to preserve geologists’ time and to help determine the best locations to fulfill their drilling strategy.

Since establishing a foundation data science and cognitive intelligence team in 2015, we have made rapid progress.

Willow , Woodside developed their own cognitive assistant based on Watson deep learning artificial intelligence platforms. In deployed Watson systems, Willow could work across and outside them. Using simple, natural language voice or text commands, Willow clearly knows which system holds the information we need, or related information lives in several systems, drawing relevant answers from our 64-

Using computational science and AI to end modern slavery

The conference topic is focused on the fight against forced labor, modern slavery, human trafficking, and child labor with artificial intelligence and computational science。

Using AI to Reduce False Positives and Line Stoppages for the Automotive Industry

Using AI-powered vision replace traditional machine-vision technology in the automotive industry to inspect the surface defects such as auto-body scratches and part defects like panel splits, cracks, inner-fin defects, defective automotive vents, etc. AI-powered vision performs is much better than the traditional rule-based machine vision in drastically lowering (up to 90% in some cases) the numbe

Siemens presents SIEAERO – the next generation of overhead line inspection

SIEAERO is a smart analytic software developed by Siemens for overhead line inspection. It leverages UAVs, AI, and digital to improve service of transmission lines. SIEAERO service is fully automated, faster, and more precise. It reduced the time for flight execution and data analysis from weeks or even months to a few days.

Use of Artificial Intelligence to Facilitate Employment Opportunities for People with Disabilities

EARN developed a policy brief for businesses interested in AI to benefit is to guarantee the right of people with disability.

Road Weather Information Systems (RWIS)

Road weather information system (RWIS) is an intelligent system which can monitoring and recording all kinds of weather information or road conditions, thus helping drivers to avoid severe pavement condition, and engineers to schedule the maintenance. The system is very sustainable and resilient as it consumes low power and is built strong enough to undergo stressful situations.

AI Reduces Data Centre Cooling Bill by 40%

Google is working with machine learning engineers from DeepMind, the world-leading artificial intelligence company, to reduce the power consumption for cooling down its considerable number of data centres. The system analyzes the relationship between various actions and their corresponding energy consumption, to select the best strategy which still maintains the safety standards.

The City Brain project, created by DAMO Academy, Alibaba, aims to solve emerging challenges Chinese cities are facing with during their rapid development, including heavy traffic congestions, urban planning, crime prevention, etc. The platform has achieved promising progress in many cities in China and Kuala Lumpur, the capital of Malaysia.

FloodAlerts

FloodAlerts is a software application which can automatically warn users about the risk of flood based on information provided by the Environment Agency. Personal users can choose a safer route by setting several monitoring locations. At the same time, the software can help enterprises to watch out for the water level in the area of infrastructure and assets.

AI in the Oil and Gas Industry

Monitor living conditions in cities using deep learning and street imagery

Researchers have applied deep learning to street imagery for measuring spatial distributions of income, education, unemployment, housing, living environment, health, and crime. It has the potential to complement traditional survey-based and administrative data sources for high-resolution urban surveillance to measure inequalities and monitor the impacts of related policies.

The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies

The AI Economist uses a Reinforcement Learning framework in simulated economies to learn dynamic tax policies, providing a purely simulation-based and data-driven solution in finding a tax policy that optimizes equality along with productivity. The simulations may help to improve policy impact and social outcomes in real-world economies.

Using AI to improve computers’ abilities to understand people with impaired speech

Google is working to create and optimize AI-based algorithms to learn about the communication needs of people with impaired speech so that "mobile phones and computers can more reliably transcribe words spoken by people with these kinds of speech difficulties."

Seeing AI App from Microsoft

Seeing AI is a Microsoft research project and also an app that helps people with visual impairment by narrating the world around them, e.g. describing the visual world around them, recognizing friends and their facial expressions, reading text on documents, etc.

Lookout App from Google

Lookout is a Google app, similar to "Seeing AI", that uses AI to detect and report items around such as people, text, objects in the scene to help people with visual disabilities.

AI sign language translator from Tencent

The AI sign language translator is developed by Tencent Youtu Lab to automatically recognize a variety of signs and translate them into written text. The device is expected to "provide better services for people with impaired hearing in public places and help develop information accessibility."

AI for culture heritage-Mont-Saint-Michel: The historic 3D model comes to life

Artificial intelligence techniques have been utilized to reconstruct the historic 3D map of the famed Mont-Saint-Michel —a 17th century icon of French.

AI for good-Media bias

Artificial intelligence is used here to identify and prevent the spread of misinformation online by quantifying bias.

TIME MACHINE

This large project aims to develop a digital information system by analyzing the big data of the past and future of Europe.

AI for culture heritage-the Māori Language Project

The project aims to preserve the dying language - te reo Māori using artificial intelligence technology.

AI for culture heritage-The Met

The Metropolitan Museum of Art houses over 1.5 million works of art spanning 5,000 years. Now, The Met is exploring artificial intelligence to make its collection accessible to the 3.9 billion internet-connected people worldwide.

Computer Science and Artificial Intelligence Laboratory-The Repaint system

The Repaint system can help to design reproductions of paintings combined with 3D printing and deep learning technologies.

Venice Time Machine

The Venice Time Machine Project is a pioneer international Digital Humanities scientific program which includes two phases to build a digital library and 4D model of Venice Mirror world.

Published in 2012

Simplified Food Waste Management

KITRO reduces avoidable food waste by providing restaurants with automated hardware and software solutions to identify, manage, and monitor the source and quantity of food waste and reduce the hidden cost of food waste.

Optimal Resource Productivity

TOMRA is a company from Norway which uses sensor-based technology for optimal resource productivity, includes reverse vending, material recovery, food sorting, recycling and mining.

Consumer-to-Manufacturer (C2M) model

With the vast database of over 300 million customers feedback, JD tries to make the consumption more responsible by using big data and artificial intelligence based on the Consumer-to-Manufacturer (C2M) model.

Today Convenience Store Project

Baidu uses the deep-learning platform PaddlePaddle and its click-through rate (CTR) model to predict the purchased quantity of fresh goods in convenience stores. This technology can effectively reduce food waste.

Home Energy Saving System

Ecoisme is an energy-saving system that could monitor and analyze the energy consumption of the electric equipment then give insights to reduce the utility bills.

Food Waste Solution

Winnow Vision uses artificial intelligence to detect the type and quantity of food discarded, and the commercial kitchen could reduce food waste with these data.

Tech For Ocean Plastic Prevention & Expanded - T.O.P.P.E.R

Building an image-recognition tool to improve plastic recycling rates, reduce ocean plastic pollution, and strengthen waste management in under-resourced communities.

Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

Using Cycle-Consistent Adversarial Networks and climate models, researchers create an educational tool that will produce accurate, vivid, and personalized street-view images of houses after extreme weather events, so as to raise public awareness and conceptual understanding of climate change.

Climate Change AI: Tackling Climate Change with Machine Learning

Experts explore how machine learning can help tackle the climate crisis, such as promoting greenhouse gas emission reduction and helping society adapt to a changing climate. Some high impact problems are identified where existing gaps can be filled by machine learning, and a platform is also built for resource and knowledge sharing as well as interdisciplinary collaborations.

ARTIFICIAL INTELLIGENCE AIDING NATURAL DISASTER RESPONSE

The World Health Organization reports that more than 160 million people are affected by natural disasters annually. Estimates from the World Bank also suggest that 26 million people are forced below the poverty line annually due to natural disasters. Technological advancements with artificial intelligence (AI) aiding natural disasters may help countries with their response to such catastrophic ev

Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets

The study presents the "first application of Deep Learning techniques as an alternative methodology for climate extreme events detection." The developed deep Convolutional Neural Network (CNN) classification system is able to learn high-level representations of a broad class of patterns from labeled data and achieves 89%-99% of accuracy in detecting extreme events.

Tracing Global Greenhouse Gas Emissions using AI

The Climate TRACE coalition is building a tool that will use artificial intelligence, satellite image processing, machine learning, and other remote sensing technologies to track worldwide human-caused greenhouse gas (GHG) emissions to specific sources in real-time, thus assisting stronger decision-making on climate change.

Reducing the carbon footprint of AI by creating “once-for-all” networks

MIT researchers have developed a new automated AI system for training and running certain neural networks which could improve the computational efficiency of the system in some key ways, thus cutting down the pounds of carbon emissions involved.

Vessel Identity

Identify illegal fishing vessels using image classification techniques.

Published in 2008

AI’s killer (whale) app

Use sound analysis to locate and protect killer whale

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Published in 2014

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Artificial Intelligence – a promising anti-corruption tool in development settings?

This study points to two different strategies for using artificial intelligence to aid anti-corruption efforts. It can be applied to uncover corruption that was previously difficult to detect. Secondly, it is possible to design novel artificial intelligence-assisted processes with the aim to avoid previously corruption-prone procedures.

Japan is Experimenting with AI to Combat Terrorism

This system can identify suspicious people and objects targeting large events, determining the model of automobiles, and analyzing suspicious financial transactions.

Baidu AI search for missing people

Face recognition technology can help to promote family reunion. The system is connected to a National Rescue System from the Ministry of Civil Affairs, and is able to search in a database containing information of 30,000 missing people. Users can upload photos of the missing ones for quick comparisons.

AI traces long-missing children

The lab-created a learning strategy to fully master the natural facial age-changing law from the data, to make cross-age recognition more reliable and accurate.

AI can detect depression in a child's speech

A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions.

Autonomous navigation of stratospheric balloons using reinforcement learning

Researches have demonstrated that stratospheric balloons guided by AI can "pursue a long-term strategy for positioning themselves about a location on the Equator even when precise knowledge of buffeting winds is not known," which would "open up a range of commercial and scientific applications for probing Earth’s atmosphere and that of other planets."

Legaltech Seed: a non-profit association to study, learn and develop content and projects related to law and technology and their impact on society.

Legaltech Seed is a Non-profit Association focused on Law and Technology, articulating the interdisciplinary participation of students, teachers and professionals in the analysis, debate and production of knowledge related to the unequal distribution in access, use and knowledge of Technologies that impacts and challenges us as citizens and professionals of law.

IALAB is an initiative whose purpose is to generate disruptive high-impact solutions in the Administration and Justice, to guarantee the effectiveness of people´s rights. Prometea, an artificial intelligence system for transforming public organizations, was developed in this context of innovation within the scope of the Public Prosecutor´s Office. It is a system that combines smart detection, prediction and smart assistance to automate the creation of documents, perform intelligent searches and assist in data control.

Assessing National Development Plans for Alignment with Sustainable Development Goals via Semantic Search

The initiative is launched by UNDP with IBM Research to develop an algorithm to automate the Rapid Integrated Assessment (RIA). The RIA aims to help countries align their national plans with the Global Goals for Sustainable Development and AI could help reduce the completion time "from an estimated 3-4 weeks to 3 days."

ALeRCE Project

With the emergence of large telescopes and astronomy cameras, the community of astronomers requires the implementation of a new generation of astronomy brokers, which are systems that serve as intermediaries between large survey telescopes, those that monitor the sky searching for new astrophysical events, and follow-up telescopes, those that observe interesting events to characterize them and understand their physical properties. Currently, these telescopes can produce up to 1 million events per night and are expected to reach up to 10 million events per night in the upcoming years. These events must be distributed among the community through the transmission of data streams so they may be captured, annotated and classified by astronomy brokers.

Project Company R1T1 Robot

R1T1 is a specialized robot for the health sector. It supports hospital staff with medical consultations and with providing patients follow-up and helps doctors perform transplants.

AI4Good: AI-based evaluation of the SDGs

Our research aims at a better understanding how technologies, organizations, and institutions associated with "artificial intelligence" (AI) hinder or foster the achievement of the sustainable development goals (SDGs). Using qualitative and quantitative methods, we integrate computer science and empirical social science. In so doing, we focus on socio-legal, accounting, and organizational analysis of advanced machine learning systems that involve data on the SDGs.

Global Infectious Disease Prevention Project

The Global Infectious Disease Prevention Project is an infectious disease tracking system based on big data and powered by artificial intelligence.

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• 17 June 2024
• Update 19 June 2024

The Sustainable Development Goals: can they be made smarter?

You have full access to this article via your institution.

The United Nations’ SDGs have transcended the policy world and entered the public consciousness. Credit: Noriko Hayashi/New York Times/Redux/eyevine

When a well-thought-out plan isn’t succeeding, what should the response be? Abandon the plan entirely? Hope that it just needs more time to work? Or get to grips with why it’s not working and make changes accordingly?

In the case of the United Nations’ Sustainable Development Goals (SDGs), giving up cannot be an option . However, this global plan to end poverty and achieve environmental sustainability is clearly not working. None of the 17 goals, which include combating climate change and reducing inequality, is expected to be achieved by the UN’s 2030 deadline. Only about 12% of the 169 underlying targets are likely to be met. For example, 2.2 billion people around the world lack access to safe drinking water and more than 300 million people go to bed hungry every night. The stated aim of the SDGs is to drive both numbers to zero.

Extending the Sustainable Development Goals to 2050 — a road map

Is it possible to improve on the existing approach? In a Comment article this week , a group of researchers from institutions in Europe and the United States suggest a combination of responses. The researchers propose that the 17 goals should all remain the same, as should many of the targets and indicators for those targets. But they are also calling for greater ambition. The goal to end poverty should include providing social protection for vulnerable people, for example. The goal for zero hunger should also tackle undernutrition.

In other cases, they suggest that actions should align with international agreements — such as the Paris climate agreement’s commitment to achieve net-zero emissions by 2050, so that global temperatures do not exceed 1.5 °C above pre-industrial levels.

There are other lessons to be learnt since the SDGs were first agreed in 2015, such as the potential impacts of artificial intelligence (AI), the authors say. One study finds that AI could benefit 134 targets across all the goals, such as making better weather forecasts or improving medical diagnoses, but that AI could also inhibit 59 targets by, for example, fuelling the spread of disinformation ( R. Vinuesa et al . Nature Commun. 11 , 233; 2020 ).

Their advice is well timed. Talks on a post-2030 future for the SDGs have not yet officially begun, but because adjustments to them cannot be made quickly, the earlier that discussions can begin, the better. Any new indicator would need to meet the UN Statistical Commission’s criteria of being conceptually clear and having an internationally established methodology and agreed standards. All relevant data, moreover, would need to be regularly produced by a large proportion of countries.

Shortly after the SDGs were settled, only around 60% of the indicators had an agreed methodology and standards. Achieving this for the remaining indicators took four years. At the latest tally (in 2022), data are still not being produced by all countries for one-third of the indicators, often because of a lack of funding or because of other constraints (see ‘The $100m data drop’).

Source: OECD/Paris21

For these and other reasons, some of the goals are still not being assessed using quantitative measures. The standout example is SDG 13, the goal for climate action, which lacks a measurable target for reducing greenhouse-gas emissions. To be part of the SDGs, national emissions would be reported annually and to a standard to be defined by expert bodies and then agreed by all member states — as would all other newly proposed targets and indicators.

Collaborate, collaborate

There’s a second reason why this proposal is well timed. UN secretary-general António Guterres has invited representatives of world leaders to gather in New York City this September for a meeting called the Summit of the Future. A draft of the document to be agreed on at this event — called the Pact for the Future — refers to a proposal to identify 10–20 SDG-like indicators of economic growth, well-being and sustainability. Few of the SDGs have the priority, status and attention in national policymaking that SDG 8 (economic growth) does. Guterres wants to change this and get policymakers to focus not just on economic indicators such as gross domestic product (GDP), but on a dashboard of indicators that he is calling Beyond GDP .

The world’s goals to save humanity are hugely ambitious — but they are still the best option

It’s important that this idea does not compete with the SDGs. That would be unproductive, given that both have similar aims. It is not surprising that this has happened — the UN is a large organization that is both complex and highly siloed. But with the opening of a debate about how best to iterate the SDGs, there is now an opportunity to allow these two processes to converge.

The SDGs ought to “remain at the centre of global policy agendas”, as the authors of the Comment article argue. The international community would then choose its favoured 10–20 indicators from any updated list. A huge amount of work has gone into creating and refining the SDGs. Guterres’s parallel effort will benefit by linking to the SDGs, taking advantage of almost a decade of accumulated learning, rather than trying to reinvent the wheel.

A big win of the SDGs is that they have transcended the world of policymakers. Their multicoloured logos can be found everywhere from classrooms to company websites. They took a long time to negotiate, and a much has gone into their refinement. Any future efforts must build on what the world has learnt — while not losing the sense of urgency that comes with the existing deadline.

Nature 630 , 529 (2024)

doi: https://doi.org/10.1038/d41586-024-02023-2

Updates & Corrections

Update 19 June 2024 : The graphic in this Editorial has been updated to include more recent information on international grants and loans

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Walking the Talk Leading Academics Urge UN to Extend, Reframe SDGs

Published 1 day ago. About a 6 minute read. Image: Eduardo Kobra

10 distinguished climate researchers have called on the UN to give its 2030 Agenda for Sustainable Development a much-needed refresh — by extending the timeline to 2050 and providing greater clarity around specific goals, among other recommendations.

A group of leading academics is calling for the UN Sustainable Development Goals ( SDGs ) to be extended past their 2030 target date and updated — with more input from communities affected by the goals and consideration for the potential impacts of disruptive technologies such as artificial intelligence ( AI ), among other recommendations.

State of the SDGs

Halfway to the original, targeted completion date of 2030, many of the 17 SDGs are off track or progressing too slowly — due to, among other things, the slowing of the global economy by COVID and international conflicts. In the run-up to the UN’s Summit of the Future in New York in September, the authors are recommending the goals be extended to 2050 and updated with greater clarity around specific goals — such as those pertaining to climate and "planetary health" — and reworking individual targets for each goal.

Enacted in 2015, the Sustainable Development Goals cover global challenges including hunger, poverty, health, climate action, education, gender equality, peace and biodiversity — with each goal comprised of specific targets.

Some goals and regions have progressed faster than others, but most of the world is lagging behind — particularly on climate and biodiversity targets — and many targets are too vague and difficult to measure. Another key challenge is to prevent some countries from progressing at the expense of others — since, as the authors point out, there are insufficient finance mechanisms in place to enable low- and middle-income countries to achieve the SDGs.

"Achieving the social and economic goals can't be done at the expense of planet," says Francesco Fuso-Nerini — director of the Climate Action Center at Sweden ’s KTH Royal Institute of Technology , honorary researcher at the University of Oxford , and lead author of the paper. "What keeps getting left behind are the climate and biodiversity goals ; so, the base layer for achieving the SDGs is a healthy planet that can support the achievement of all social and economic goals."

Fuso-Nerini co-authored the call to action — published in Nature — with Mariana Mazzucato , University College London ; Johan Rockström , Potsdam Institute for Climate Impact Research and the Earth Commission ; Harro van Asselt , University of Cambridge ; Jim W. Hall , University of Oxford; Stelvia Matos , University of Surrey ; Åsa Persson , Stockholm Environmental Institute ; Benjamin Sovacool , Boston University ; Ricardo Vinuesa , KTH Royal Institute of Technology; and Jeffrey Sachs , Columbia University .

The authors point out that action towards the SDGs has often been siloed and strategies unaligned. For example, as well as increasing spending on health, many COVID-19 recovery packages poured money into shoring up carbon-intensive industries  rather than boosting renewables . And only a handful of countries’ climate commitments under the Paris Agreement take into account broader SDG outcomes — including impacts on incomes, poverty, jobs, inequality, health and education.

Time for a refresh

The authors highlight an urgent need for updated pathways and milestones toward reaching all SDGs — including integrating net-zero greenhouse gas emissions, as well as strengthening global governance of the transition through existing frameworks agreed upon by the members of the UN — and that adapting the goals to be relevant to 2050 will require more inclusive consultations with scientists, indigenous populations, marginalized communities and the private sector.

They also point to other critical developments — such as the rise of AI — that have been introduced since the SDGs were first agreed in 2015. One study finds that AI could benefit 134 targets across all the goals — such as making better weather forecasts or improving medical diagnoses — but that it could also inhibit 59 targets by fueling climate change, energy use and the spread of disinformation .

Among steps toward strengthening the goals, they recommend reframing them as missions with clear targets and reforming global finance architecture to enable more public investment in reaching the goals.

In the face of the lack of coordinated, significant, strategic progress toward the Goals, the authors say, “Some people have argued that the world should take stock and focus on fewer sustainability goals and targets. We disagree. Because all of these global crises are interlinked, only a holistic and global approach to solving them will work. The SDGs should remain at the center of global policy agendas.”

Summit of the Future

UN Secretary-General António Guterres has invited representatives of world leaders to gather in New York City this September for a meeting called the Summit of the Future, the goal of which is to agree on an updated draft of a document called the Pact for the Future — a proposal to identify 10–20 SDG-like indicators of economic growth, wellbeing and sustainability. The authors point out that few of the SDGs have the priority, status and attention in national policymaking that SDG 8 ( decent work and economic growth ) does. Guterres wants to change this and get policymakers to focus not just on economic indicators such as GDP, but on a dashboard of indicators that he is calling Beyond GDP .

“We call on member states of the United Nations, in the run-up to the UN Summit of the Future in September, to adapt and extend the SDG framework to 2050,” the authors say. “This will entail setting interim targets for 2030 and 2040 and final targets for 2050 that align with science and maintain high, yet achievable, national and global ambitions.”

They urge the UN General Assembly to review and adopt new guidelines by 2026; and that all nations should prepare revised, comprehensive and forward-looking voluntary national reviews of SDG strategies, along with quantifiable interim targets no later than 2027.

First things first

Among recommendations for revised global actions and timelines for all of the SDGs and the targets within them, the authors highlight several critical priorities:

Scientists must set out pathways for updating SDG targets and milestones to return Earth to a safe operating zone within two decades.

Global greenhouse gas emissions must reach net zero by 2040–50 .

Global biodiversity loss must be halted in the next decade , and investments made to protect and regenerate intact and managed ecosystems.

Patterns of resource extraction and use , covering everything from rare-earth metals to construction materials and nutrients, must shift towards circular models .

All economic transactions must account for the true cost of planetary damage .

Published Jun 24, 2024 8am EDT / 5am PDT / 1pm BST / 2pm CEST

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