The collaborative power of AI and citizen science in advancing the Sustainable Development Goals

The SDGs were launched in 2015 to guide global efforts toward sustainability by 2030. However, as this deadline nears, many countries still lack the data needed to track SDG progress. For example, data are missing for nearly half of the 92 environmental indicators, only 15% of targets are on track, and all SDG targets suffer from insufficient data. Other challenges include poor data quality, limited data sharing, infrequent data collection, and lack of local data, hindering targeted interventions.

The perspective piece authored by IIASA researchers and published in Nature Sustainability, explores how combining the collaborative strengths of citizen science with AI can enhance both SDG monitoring and achievement.

Citizen science is already contributing to the SDGs by helping to address data gaps through public participation in scientific research. Successful applications have been demonstrated for SDGs 3 (good health and wellbeing), 11 (sustainable cities and communities), 14 (life below water), and 15 (life on land). However, despite increasing interest from the UN, National Statistical Offices (NSOs) and government agencies, challenges around data quality, lack of awareness and legal frameworks continue to limit the integration of citizen science data into SDG monitoring and reporting, and ultimately for informing policy decisions.

In parallel, recent advancements in AI have sparked interest in its potential to support sustainable development and address data challenges faced by NSOs and international organizations. AI’s major contributions to SDG progress include rapid analysis of large datasets, enhanced data accessibility, efficient data collection, task automation, real-time data and insights, and improved data visualization – potentially in a more cost-efficient way. Nonetheless, AI poses challenges and risks, including biases in training data that can produce unreliable results.



The authors propose that citizen science approaches can help mitigate AI risks by providing more localized and disaggregated, thus representative data.

“AI algorithms require large amounts of data, yet many parts of the world, especially the Global South, face data shortages. This lack of data, especially local data, can lead to AI models that don't reflect specific local contexts, resulting in inaccurate findings, biases, and widening disparities between the Global North and South, as well as within countries,” explains Dilek Fraisl, lead author of the perspective piece and researcher in the Novel Data Ecosystems for Sustainability Research Group of the IIASA Advancing Systems Analysis Program. “Citizen science can help address this gap by providing more local and thus representative data, which can help improve the accuracy of AI results.”

Fraisl further explains that AI models are only as reliable as the data they are trained on, and any biases in this data can cause misleading results. So, while AI has great potential, its benefits will only be fully realized if its biases and limitations are carefully addressed.

The recent adoption of the Global Digital Compact within the UN's Pact for the Future, a framework outlining principles, objectives, and actions for advancing an open, free, secure and human-centered digital future for all, highlights the need for global cooperation in AI governance. This framework emphasizes AI's role in achieving sustainable development while also warning of its risks, such as potential threats to human rights. Incorporating citizen science approaches into AI can be a crucial step towards addressing these risks and ensuring that AI serves the common good.

“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. However, careful attention to inclusivity, representation, and governance is essential to harnessing these tools in a way that genuinely benefits all,” concludes Fraisl.

Reference:

Fraisl, D., See, L., Fritz, S., Haklay, M., McCallum, I. (2024) Leveraging the collaborative power of AI and citizen science for sustainable development Nature Sustainability DOI: 10.1038/s41893-024-01489-2