By Dilek Fraisl, Senior 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) offer immense potential for tackling urgent sustainability challenges, from health to climate change. Combined, they offer innovative solutions to accelerate progress on the SDGs.
A new paper explores the synergies between citizen science and AI, specifically highlighting how the integration of citizen science data and approaches into AI development can enhance sustainable development monitoring and achievement while mitigating AI risks.
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 scientists at the International Institute for Applied Systems Analysis (IIASA) and University College London (UCL) 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 well-being), 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 suggest 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,” 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. “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,” she notes. “Citizen science can help address this gap by providing more local and thus representative data, which can help improve the accuracy of AI results.”
AI models are only as reliable as the data they are trained on, and any biases in these 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,” says Fraisl. “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,” she concludes.
Read the paper here.