Report Overview
The "AI for Good Impact Report" is a comprehensive analysis published by the International Telecommunication Union (ITU) in partnership with Deloitte. The report explores how artificial intelligence, particularly generative AI, can accelerate progress across all 17 United Nations Sustainable Development Goals (SDGs).
Key Insight: AI holds great promise in accelerating the achievement of the UN Sustainable Development Goals. Its rapid development is helping to reshape industries, economies, and societies around the world. However, its dual nature—offering both transformative potential and significant risks—requires careful consideration and responsible governance.
Key Data Points
Key Insights Summary
AI's Transformative Potential for SDGs
AI has demonstrated its ability to drive meaningful advancements in critical areas ranging from healthcare and education to climate change and accessibility. AI is being used in nearly 400 projects across the UN system, spanning all 17 SDGs.
Balancing Opportunities and Risks
While AI offers transformative potential, it also comes with considerable challenges including ethical concerns such as bias and privacy, social disruptions like misinformation and job displacement, and technical matters like data quality and interpretability.
Global AI Governance Landscape
The global landscape for AI governance is swiftly advancing, with frameworks emerging and evolving to guide the development and deployment of AI systems. Central to these frameworks is a commitment to upholding ethical principles and human rights.
Workforce Transformation
AI is reshaping the workforce by automating routine tasks, shifting job roles, and augmenting human abilities. Research suggests that a maximum of 2.3% of global jobs could be fully automated, but this doesn't account for new jobs created by the technology.
Regional Adoption Variations
AI adoption varies considerably between regions, influenced by factors such as technological advancements, economic potential, and investment in AI research and development. Companies from the UAE, China, India, and Singapore are leading in actively deploying AI.
Sustainability Considerations
The significant energy consumption of AI systems poses environmental concerns. Training large AI models requires vast amounts of electricity, and AI's impact on water resources and waste creation is also largely underestimated.
Content Overview
Document Contents
Foreword by ITU and Deloitte
We are at a turning point. As Heads of State and Government have recognized in the Global Digital Compact, part of the newly adopted UN Pact for the Future, "the pace and power of emerging technologies are creating new possibilities but also new risks for humanity."
Nowhere is this more evident than with artificial intelligence. Machine-learning models are forecasting weather faster and more accurately, helping us better prepare for the impacts of climate change. AI-powered brain-machine interfaces are giving ALS patients their voice back – just one of many groundbreaking innovations poised to transform healthcare.
At the same time, AI-driven automation risks making millions around the world more vulnerable to job displacement. Photos, including those of minors, are being scraped off the web to create powerful AI tools, often without consent. Deepfakes and misinformation are blurring reality and eroding public trust.
Executive Summary
Artificial intelligence (AI) holds great promise in accelerating the achievement of the United Nations Sustainable Development Goals (SDGs). Its rapid development is helping to reshape industries, economies, and societies around the world. Yet, its dual nature–offering both transformative potential and significant risks – requires careful consideration.
AI has demonstrated its ability to drive meaningful advancements in critical areas ranging from healthcare and education to climate change and accessibility. Today, AI is being used in nearly 400 projects across the UN system, spanning all 17 SDGs. However, achieving the SDGs by 2030 is currently off track. With the right approach, AI could be a key driver in getting back on track to meet these global objectives.
Key barriers to broader adoption globally include insufficient technical skills, the need for extensive upskilling and reskilling, and varying levels of trust in AI technologies. Despite these challenges, the potential benefits are substantial, from automating routine tasks to augmenting human capabilities.
Introduction
This AI for Good Impact Report provides an overview of AI trends, governance, and opportunities to support informed decision-making across sectors. It explores global AI regulation and frameworks showing different approaches to managing AI's risks and potential.
The report outlines challenges associated with AI and its responsible integration, including established and emerging good practices from around the world. It also examines the role of AI in accelerating progress towards the Sustainable Development Goals (SDGs).
Designed for government officials, policymakers, NGOs, international development organizations (IDOs), and industry leaders, this report serves as a valuable tool to guide the adoption and scaling of ethical AI initiatives.
AI and Generative AI Trends
The term "artificial intelligence" (AI), coined almost seven decades ago, has garnered significant public attention in recent years due to its synergies with individuals, businesses, governments, and legislation.
From a technical perspective, AI encompasses several key areas, each representing different stages and methodologies in its development. The first wave, symbolic AI, involves early techniques that rely on predefined rules and logic. The second wave, characterized by machine learning (ML) and data-driven AI, uses large datasets to train algorithms that can make predictions or decisions without explicit programming.
For companies, AI adoption is increasingly vital, with 94% of global business leaders viewing AI as critical for their organization's success in the next five years. Global AI market revenue is projected to grow by a 19% Compound Annual Growth Rate (CAGR) over the next decade, surpassing US$2 trillion by 2031.
AI Regulations and Frameworks
AI has evolved rapidly from a specialized technology to a key element of modern industry, governance, and society. However, this rapid expansion of AI technology also brings significant risks, including ethical concerns like algorithmic bias and privacy violations.
In response to the opportunities presented by AI as well as the risks it poses, there have been comprehensive international developments aimed at establishing comprehensive AI governance frameworks. These developments encompass a wide array of initiatives, ranging from high-level strategic planning to the formulation of codes of conduct, and even the implementation of binding regulatory measures.
The EU AI Act is the most notable approach to regulating AI on a regional level - it is the first comprehensive AI regulation. It entered into force on 1 August 2024 and is now applicable across all 27 member states of the European Union with significant extra-territorial reach for AI providers that offer their products or services on the EU market.
Addressing AI's Challenges
AI's development and implementation bring a range of social, environmental, or technical challenges, such as data privacy concerns and the significant energy consumption required to support AI systems.
Data privacy and security are critical challenges in the rapidly advancing field of AI with 80% of data experts surveyed saying that AI is making data security more challenging. AI tools use vast amounts of data from various sources to train and learn from, often personal data, and there is a risk that personal data could be integrated into the model and shared with other users.
The significant energy consumption of AI systems poses serious environmental concerns, particularly in the context of climate change. Training large AI models can require vast amounts of electricity that usually do not come from renewable sources.
Achieving the SDGs with AI
The SDGs, also known as the 2030 Agenda, include 17 goals and 169 targets that outline a sustainable future. Despite their pivotal role in driving actions towards a sustainable future, most SDGs are unfortunately not progressing as intended, with some even regressing.
AI has evolved as a technology with numerous use cases that support the alignment of the SDGs with technological advancements. For instance, AI can enhance data availability in previously unmeasurable areas, such as using satellite images to monitor deforestation.
1 No Poverty
AI can enhance the efficiency of the financial sector, increasing accessibility for the 1.7 billion adults lacking access to financial services.
2 Zero Hunger
AI offers precision farming to optimize resource use, monitoring environmental conditions, and tracking animals for their well-being.
3 Good Health
AI can enhance diagnostics by efficiently reviewing patient data and accelerate drug discovery processes.
4 Quality Education
AI provides customized learning experiences, develops digital tutors, and creates assessment tools for personalized education.
5 Gender Equality
AI can facilitate monitoring of gender equality and support platforms for women to seek help in cases of violence or abuse.
6 Clean Water
AI applications include data monitoring for water management systems and optimizing water flows to reduce energy usage.
7 Affordable Energy
AI can develop smart grids, optimize energy production, and explore new energy solutions and materials.
8 Decent Work
AI automation could generate approximately US$15.7 trillion by 2030, though it may also displace certain jobs.
9 Industry & Innovation
AI enhances research efficiency, provides access to previously unusable data, and contributes to industrialization.
10 Reduced Inequality
AI can monitor situations for at-risk communities and aid refugee support, though ownership concentration remains a concern.
11 Sustainable Cities
AI promotes environmental sustainability through energy efficiency and improves urban transportation systems.
12 Responsible Consumption
AI supports the green transition through process optimization and driving circularity in organizations.
13 Climate Action
AI optimizes logistics to minimize CO₂ emissions and provides improved forecasting for weather events.
14 Life Below Water
AI monitors underwater biodiversity, develops new transporting solutions, and helps identify oil spills.
15 Life on Land
AI applications include monitoring land-use change, counting biodiversity, and assisting in anti-poaching efforts.
16 Peace & Justice
AI drives efficient justice systems, improves government record-keeping, and enhances national security.
17 Partnerships
AI supports brainstorming activities, analyzes real-time data for international action, and assists in tax compliance.
Conclusion
AI holds immense potential to drive positive change, but like any tool, its impact depends on how it is used. Governments, businesses, and society must work together to steer AI toward advancing social good rather than causing harm.
To achieve this, governments must take proactive steps. First, aligning AI development with sustainable goals and following best practices will ensure that new use cases contribute to social and environmental progress. This means creating policies that incentivize AI projects that focus on long-term societal benefits over short-term profits.
Transparency is another key aspect. AI systems must be open about how they work and which datasets they use to avoid biases or abuses in decision-making. Clear regulations should be in place to ensure ethical AI deployment.
Finally, access to AI technology should be broad to ensure that the benefits of AI are shared globally, not just concentrated in wealthier countries or large corporations. At the same time, ongoing technological improvements are needed to minimize AI's environmental footprint.
Glossary
Algorithm: A set of instructions or rules that serve as the foundation of programming and software development.
Artificial intelligence (AI): The branch of computer science focused on creating systems capable of performing tasks that typically require human intelligence.
Bias: Systematic errors in a model that lead to unfair or incorrect predictions, often due to imbalanced training data or flawed algorithms.
Deep learning: A subset of machine learning that uses neural networks with many layers to model complex patterns in data.
Generative AI (GenAI): A class of artificial intelligence models that can create new content, such as text, images, music, or code.
Machine learning (ML): A subset of AI that involves the development of algorithms that enable computers to learn from and make predictions based on data.
Note: The above is only a summary of the report content. The complete document contains extensive data, charts, and detailed analysis. We recommend downloading the full PDF for in-depth reading.