Synergy of AI and Citizen Science: A Powerful Tool for Sustainable Development Goals

The Power of Collaboration: AI and Citizen Science

In a groundbreaking perspective piece published in Nature Sustainability, researchers from the International Institute for Applied Systems Analysis (IIASA) have highlighted the immense potential of combining artificial intelligence (AI) and citizen science to address urgent sustainability challenges 1

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. This innovative approach could significantly accelerate progress towards the United Nations Sustainable Development Goals (SDGs), offering solutions to complex issues ranging from health to climate change.

Addressing Data Gaps in SDG Monitoring

As the 2030 deadline for achieving the SDGs approaches, many countries still struggle with insufficient data to track their progress. Nearly half of the 92 environmental indicators lack data, and only 15% of targets are on track 1

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. Citizen science has already demonstrated its ability to contribute to SDGs by addressing these data gaps through public participation in scientific research.

AI's Contributions and Challenges

Recent advancements in AI have sparked interest in its potential to support sustainable development. AI's major contributions include:

1. Rapid analysis of large datasets
2. Enhanced data accessibility
3. Efficient data collection
4. Task automation
5. Real-time data and insights
6. Improved data visualization

However, AI also poses challenges, particularly in terms of biases in training data that can produce unreliable results 1

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Mitigating AI Risks through Citizen Science

The researchers propose that citizen science approaches can help mitigate AI risks by providing more localized and disaggregated data. This is particularly crucial in addressing the data shortage in many parts of the world, especially the Global South 2

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Dilek Fraisl, lead author of the perspective piece, explains, "Citizen science can help address this gap by providing more local and thus representative data, which can help improve the accuracy of AI results" 1

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Case Study: Marine Plastic Litter in Ghana

A recent study demonstrated how an AI algorithm created in Europe could identify marine plastic litter along Ghana's coastline using drone imagery. However, the study also highlighted the importance of refining the algorithm with local data to effectively identify context-specific litter items 2

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Addressing AI Biases

AI can exhibit and emphasize societal biases related to race, color, gender, disability, and ethnic origin. Citizen science approaches can be leveraged to address such biases by increasing the availability of data that reflect realities rather than prejudices 2

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Future Developments and Opportunities

The integration of AI and citizen science is expected to bring a wealth of new opportunities and varied applications. For instance, generative AI could transform how citizen science applications are developed and how citizen scientists interact with them, transitioning from static to conversational interfaces 2

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Global Digital Compact and AI Governance

The recent adoption of the Global Digital Compact within the UN's Pact for the Future emphasizes AI's role in achieving sustainable development while also warning of its risks. Incorporating citizen science approaches into AI can be a crucial step towards addressing these risks and ensuring that AI serves the common good 1

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As Fraisl concludes, "The integration of citizen science and AI offers a promising path forward in SDG monitoring and achievement. When used together, AI's analytical power and citizen science's contextual relevance create synergies that can address sustainability challenges more effectively" 1

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