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The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality

Author

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  • Maryam Lotfian

    (Institute INSIT, School of Business and Engineering Vaud, University of Applied Sciences and Arts Western Switzerland, 1400 Yverdon-les-Bains, Switzerland
    Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy)

  • Jens Ingensand

    (Institute INSIT, School of Business and Engineering Vaud, University of Applied Sciences and Arts Western Switzerland, 1400 Yverdon-les-Bains, Switzerland)

  • Maria Antonia Brovelli

    (Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy)

Abstract

Advances in artificial intelligence (AI) and the extension of citizen science to various scientific areas, as well as the generation of big citizen science data, are resulting in AI and citizen science being good partners, and their combination benefits both fields. The integration of AI and citizen science has mostly been used in biodiversity projects, with the primary focus on using citizen science data to train machine learning (ML) algorithms for automatic species identification. In this article, we will look at how ML techniques can be used in citizen science and how they can influence volunteer engagement, data collection, and data validation. We reviewed several use cases from various domains and categorized them according to the ML technique used and the impact of ML on citizen science in each project. Furthermore, the benefits and risks of integrating ML in citizen science are explored, and some recommendations are provided on how to enhance the benefits while mitigating the risks of this integration. Finally, because this integration is still in its early phases, we have proposed some potential ideas and challenges that can be implemented in the future to leverage the power of the combination of citizen science and AI, with the key emphasis being on citizen science in this article.

Suggested Citation

  • Maryam Lotfian & Jens Ingensand & Maria Antonia Brovelli, 2021. "The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:8087-:d:597605
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    Cited by:

    1. Koehler, Maximilian & Sauermann, Henry, 2024. "Algorithmic management in scientific research," Research Policy, Elsevier, vol. 53(4).

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