Futures

Harnessing AI for Social Good: Opportunities and Challenges in Achieving the UN SDGs, (from page 20240811.)

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Summary

The rapid advancement of AI over the past two years has shown its potential to address global challenges, particularly in relation to the UN Sustainable Development Goals (SDGs). AI is currently being utilized to enhance efforts across all 17 SDGs, with notable applications in health, education, climate action, and sustainable cities. Despite this, significant disparities exist in funding and deployment, particularly favoring higher-income regions. Challenges such as data quality, accessibility, and talent shortages hinder the scaling of AI initiatives for social good. Experts emphasize the need for collaboration among various stakeholders to harness AI effectively and address pressing issues like poverty, education, and environmental sustainability. The report highlights the importance of directing resources toward AI projects that can benefit lower-income countries, where the impact could be substantial.

Signals

name description change 10-year driving-force relevancy
Increase in AI Use Cases Rapid growth in AI use cases from 170 to about 600 for social good. Shift from limited AI use to vast applications for social benefit. In 10 years, AI could be integral to addressing global social issues and improving lives. The need for sustainable development and social equity drives AI innovation. 4
Geographic Disparities in AI Funding Only 10% of AI grants aimed at SDG initiatives went to low/middle-income countries. From unequal funding distribution to more equitable resource allocation globally. In a decade, funding might be more accessible to underrepresented regions, enhancing local AI solutions. The push for inclusivity and equitable development in AI funding motivates changes in allocation. 5
Focus on Research Over Deployment Most AI social good efforts focus on research rather than scaling solutions. Transition from research-centric initiatives to large-scale AI solutions deployment. In 10 years, scalable AI solutions may dominate, leading to widespread social impact. The demand for practical applications of AI in real-world scenarios drives this shift. 4
Potential for AI in Education Significant opportunities exist for AI funding in Quality Education despite low current investment. From limited investment in educational AI to increased funding and innovative projects. AI could transform education accessibility and quality, particularly in underserved areas. The urgent need for quality education drives interest in AI solutions. 4
AI for Climate Action AI’s role in Climate Action (SDG 13) is growing, especially in energy efficiency. Shift from traditional methods to innovative AI solutions for combating climate change. In 10 years, AI could be central to global climate strategies and sustainability efforts. The increasing severity of climate issues propels AI adoption for environmental solutions. 5
Expert Concerns About AI Misuse Experts highlight risks of AI misuse, including bias and misinformation. From a focus on AI potential to awareness of ethical implications and risks. In a decade, enhanced regulations and ethical frameworks may govern AI use in society. The need for responsible AI deployment drives ethical considerations to the forefront. 4

Concerns

name description relevancy
Inaccurate AI outputs The risk of AI generating inaccurate or misleading information, impacting decision-making and trust. 5
Bias in AI training data The potential for biases in training data to result in unfair or discriminatory AI outcomes. 5
Misinformation proliferation The possibility of AI being used to spread false information, negatively affecting public perception and behavior. 5
Privacy and security issues Concerns over data privacy and security due to the extensive use of AI in various sectors. 5
Job displacement The risk of significant job losses as AI automates tasks traditionally performed by humans. 4
Geographic disparities in AI implementation The unequal distribution of AI benefits, favoring high-income countries over low and middle-income ones. 4
Challenges in data availability and quality Persistent issues with the accessibility and quality of data necessary for effective AI deployment. 4
Organizational resistance to change Resistance from organizations in adopting and scaling AI technologies for social good. 4

Behaviors

name description relevancy
AI for Social Good AI technologies are increasingly deployed to address global challenges, particularly in achieving UN Sustainable Development Goals (SDGs). 5
Collaborative AI Deployment Stakeholders are encouraged to work together to scale AI solutions for societal and environmental benefits, emphasizing partnerships across sectors. 4
Focus on Underrepresented Regions There is a growing recognition of the need to direct AI initiatives and funding towards lower-income countries to maximize impact. 4
Increased Use Cases for AI The number of identified use cases for AI in promoting social good has surged, indicating a broader acceptance and application of AI technologies. 5
Addressing AI Risks Organizations are beginning to prioritize awareness of risks associated with AI deployment, such as biases and misinformation. 4
Funding Allocation Disparities A significant gap exists in funding for AI initiatives between high-income and low/middle-income countries, highlighting inequities in resources. 4
Emphasis on Research to Practice There is a shift needed from research-focused AI initiatives to larger-scale deployment and practical applications in the field. 5

Technologies

name description relevancy
Artificial Intelligence (AI) AI’s rapid advancements enable large-scale deployment across various sectors to promote social good and address challenges. 5
Generative AI Generative AI expands possibilities in AI applications, enhancing productivity and social impact. 5
Natural Language Processing A subset of AI, it facilitates communication and understanding between humans and machines. 4
Sound Recognition AI technology capable of interpreting and understanding sound, useful in various applications. 4
Tracking Technologies Technologies that utilize AI for monitoring and data collection, aiding in various sectors including health and environmental protection. 4
Autonomous Vehicles Vehicles that use AI to navigate and operate independently, contributing to clean energy and efficient transport. 5
Open-Sourced AI Applications AI models and applications that are accessible to all, promoting collaboration and innovation. 4

Issues

name description relevancy
Scaling AI for Social Good Challenges persist in scaling AI initiatives focused on social good, with most efforts emphasizing research over large-scale deployment. 4
Geographic Disparities in AI Funding A significant portion of AI funding is concentrated in higher-income countries, leaving lower-income regions with minimal support despite greater needs. 5
Data Quality and Accessibility Data availability, accessibility, and quality are critical barriers to the effective deployment of AI for social good initiatives. 4
Risks of AI Misuse AI tools, despite being designed for social good, can be misused, raising concerns about bias, misinformation, and privacy. 5
Underfunding in Key SDGs Certain SDGs, particularly Quality Education and Climate Action, are receiving relatively low private capital investments, indicating missed opportunities. 4
Talent Accessibility in AI The availability and accessibility of AI talent remain significant challenges for scaling AI applications effectively. 4
Collaboration for AI Solutions There is a pressing need for stakeholders to collaborate to maximize AI’s potential to address global challenges effectively. 5