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.
Highlights
Accepted: 7 July 2021The simulation of human intelligence in machines, known as artificial intelligence (AI), is widely applied in various domains, and the number of scientific publications in this area are significantly increasing [1]
The counting is done by both citizen scientists and machines, and while the results indicate that the machine performance is faster and more accurate than the human, the authors state that the citizen scientists’ contributions are essential in providing training data to feed the algorithm
There are potential challenges to and ideas about this subject that can be seen as future extensions of this integration, some of which can be performed in the near future of citizen science, and others requiring more time and investigation before being implemented in practice
Summary
The simulation of human intelligence in machines, known as artificial intelligence (AI), is widely applied in various domains, and the number of scientific publications in this area are significantly increasing [1]. The opportunities and challenges of merging ML and citizen science have been addressed in a few recent articles [8,9,10], the main emphasis has been on the transparency of using ML in citizen science in terms of how the ML algorithms use citizen science data [10], the effects of AI on human behavior and improving insights in citizen science [8], and the effects of this combination in ecological monitoring in terms of having cheaper or more efficient ways for data collection and data processing [9] While these are key issues to explore, to the best of our knowledge, the integration of ML and CS has received less attention in terms of how this integration can affect the usual processes in a CS project, from volunteer involvement to influencing the quality of their contributions.
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