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Leveraging the collaborative power of AI and citizen science for sustainable development

Authors: Fraisl, Dilek; See, Linda; Fritz, Steffen; Haklay, Mordechai; McCallum, Ian;

Leveraging the collaborative power of AI and citizen science for sustainable development

Abstract

Both artificial intelligence (AI) and citizen science hold immense potential for addressing major sustainability challenges from health to climate change. Alongside their individual benefits, when combined, they offer considerable synergies that can aid in both better monitoring of, and achieving, sustainable development. While AI has already been integrated into citizen science projects such as through automated classification and identification, the integration of citizen science approaches into AI is lacking. This integration has, however, the potential to address some of the major challenges associated with AI such as social bias, which could accelerate progress towards achieving sustainable development.

The definitive version of this paper is available at: https://doi.org/10.1038/s41893-024-01489-2 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, vol. 8, p. 125–132. https://doi.org/10.1038/s41893-024-01489-2

Countries
France, Austria
Keywords

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], 570, Artificial intelligence, sustainable development, Citizen Science, Climate Change, Sustainability sciences, [SDE.ES] Environmental Sciences/Environment and Society, 500, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC]

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
23
Top 10%
Top 10%
Top 10%
Green