Unlocking the Synergy of AI and Citizen Science to Achieve Sustainable Development Goals

By Dilek Fraisl, Research Scholar at the International Institute for Applied Systems Analysis (IIASA), and Managing Director of the Citizen Science Global Partnership (CSGP)

Citizen science and artificial intelligence (AI) have a significant role to play in solving urgent sustainability issues, such as health and climate change. Together, they can create new ways to speed up progress on the Sustainable Development Goals (SDGs).

A recent paper examines how combining citizen science and AI can improve the tracking and achievement of SDGs while reducing risks associated with AI use. The SDGs were established in 2015 to provide a global roadmap for sustainability by 2030. However, time is running out, and many countries still struggle with data collection for these goals. Alarmingly, nearly half of the 92 environmental indicators lack sufficient data, with only 15% of targets on track.

The study, published in Nature Sustainability, highlights the power of citizen science in data collection. Public involvement in scientific research fills data gaps, which is crucial for the SDGs. Successful citizen science projects contribute to various SDGs, such as good health (SDG 3), sustainable cities (SDG 11), life below water (SDG 14), and life on land (SDG 15). Yet, challenges persist, including poor data quality, limited sharing, irregular data collection, and insufficient local data.

On the other hand, AI has shown promise in supporting sustainable development. It can quickly analyze large datasets, enhance accessibility, streamline data gathering, automate tasks, provide real-time insights, and improve data visualization—all potentially at a lower cost. However, AI also has its drawbacks, such as biases in the data it uses, which can lead to unreliable outcomes.

The authors of the paper argue that citizen science can mitigate some of these AI risks by delivering localized and representative data. Dilek Fraisl points out that many regions, especially in the Global South, face data shortages that can skew AI models, leading to unfair biases and widening the gap between richer and poorer areas. By using citizen science, we can gather valuable local data, improving the accuracy of AI-driven insights.

It’s critical to remember that AI can only be as reliable as the data it uses. Any inherent biases can lead to misleading conclusions. While AI holds great potential, its success hinges on responsibly addressing these biases.

The UN’s Global Digital Compact also underscores the importance of global cooperation in AI governance. It promotes AI as a tool for sustainable development while acknowledging its risks, including threats to human rights. Incorporating citizen science can help navigate these challenges and ensure that AI serves everyone’s interests.

Fraisl concludes, “The combination of citizen science and AI presents a promising path for monitoring and achieving the SDGs. By leveraging AI’s analytical capabilities and citizen science’s local knowledge, we can tackle sustainability challenges more effectively. However, we must prioritize inclusivity, representation, and governance to ensure these tools truly benefit everyone.”

For more insights, check out the paper here.

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