Machine Learning in Citizen Science: Promises and Implications

Franzen, Martina; Kloetzer, Laure; Ponti, Marisa; Trojan, Jakub; Vicens, Julián

doi:10.1007/978-3-030-58278-4_10

Cited by 22 publications

(28 citation statements)

References 32 publications

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“…This is precisely what data mining can do for data collected by citizen scientists. Such data mining poses both challenges and opportunities (Franzen et al, 2021). In this era of digitalization and an increasingly data-oriented society, the use of data mining to understand, analyze and obtain new knowledge is inevitable.…”

Section: Future Potentials and Trendsmentioning

confidence: 99%

“…When applied to citizen scientist data, it can be used to find causal relationships or patterns in the observations, or to detect biases in the data (Chen and Gomes, 2019). The numerous examples of using data mining in citizen science projects include but are not limited to astronomy, life sciences, environmental sciences and oceanography (Franzen et al, 2021). While citizen science offers enormous opportunities, for example in training classification algorithms, there is also a need for rigorous procedures to ensure data quality (Balázs et al, 2021), as in any scientific research.…”

Section: Introductionmentioning

confidence: 99%

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TeaTime4Schools: Using Data Mining Techniques to Model Litter Decomposition in Austrian Urban School Soils

et al. 2021

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Litter decomposition plays a pivotal role in the global carbon cycle, but is difficult to measure on a global scale, especially by citizen scientists. Here, citizen scientists, i.e., school students with their teachers, used the globally applied and standardized Tea Bag Index (TBI) method to collect data on litter decomposition in urban areas in Austria. They also sampled soils to investigate the linkages between litter decomposition and soil attributes. For this study, 54 sites were selected from the school experiments and assembled into a TBI dataset comprising litter decomposition rates (k), stabilization factors (S), as well as soil and environmental attributes. An extensive pre-processing procedure was applied to the dataset, including attribute selection and discretization of the decomposition rates and stabilization factors into three categories each. Data mining analyses of the TBI data helped reveal trends in litter decomposition. We generated predictive models (classification trees) that identified the soil attributes governing litter decomposition. Classification trees were developed for both of the litter decomposition parameters: decomposition rate (k) and stabilization factor (S). The main governing factor for both decomposition rate (k) and stabilization factor (S) was the sand content of the soils. The data mining models achieved an accuracy of 54.0 and 66.7% for decomposition rates and stabilization factors, respectively. The data mining results enhance our knowledge about the driving forces of litter decomposition in urban soils, which are underrepresented in soil monitoring schemes. The models are very informative for understanding and describing litter decomposition in urban settings in general. This approach may also further encourage participatory researcher-teacher-student interactions and thus help create an enabling environment for cooperation for further citizen science research in urban school settings.

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Section: Future Potentials and Trendsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TeaTime4Schools: Using Data Mining Techniques to Model Litter Decomposition in Austrian Urban School Soils

et al. 2021

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“…Ethics highlights the challenges of potential information misuse when integrating AI. Another recent study by Franzen et al [10] also discusses the opportunities and challenges of human-computer interaction in citizen science with a focus on the concept of transparency when integrating ML in citizen science projects, which means that information about data use, ML algorithms, and data processing must be transparent and communicated to participants.…”

Section: The Influence Of ML On Citizen Science Stepsmentioning

confidence: 99%

“…Although the combination of ML and citizen science is not new [8], until recently, these two fields have mostly been implemented separately [9]. The integration of ML and citizen science can result in producing a new learning paradigm for citizen scientists through human-computer interactions [10]. Moreover, it can result in increasing interdisciplinary collaborations among researchers as well as members of the public in various fields such as computer science, ecology, astronomy, and medicine, to name a few [9].…”

Section: Introductionmentioning

confidence: 99%

“…We have reviewed successful citizen science projects, highlighting the typologies of techniques used in such projects and categorizing them in light of the effect of ML on citizen science tasks. Although 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.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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.

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Narratives of epistemic agency in citizen science classification projects: ideals of science and roles of citizens

2022

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Citizen science (CS) projects have started to utilize Machine Learning (ML) to sort through large datasets generated in fields like astronomy, ecology and biodiversity, biology, and neuroimaging. Human–machine systems have been created to take advantage of the complementary strengths of humans and machines and have been optimized for efficiency and speed. We conducted qualitative content analysis on meta-summaries of documents reporting the results of 12 citizen science projects that used machine learning to optimize classification tasks. We examined the distribution of tasks between citizen scientists, experts, and algorithms, and how epistemic agency was enacted in terms of whose knowledge shapes the distribution of tasks, who decides what knowledge is relevant to the classification, and who validates it. In our descriptive results, we found that experts, who include professional scientists and algorithm developers, are involved in every aspect of a project, from annotating or labelling data to giving data to algorithms to train them to make decisions from predictions. Experts also test and validate models to improve their accuracy by scoring their outputs when algorithms fail to make correct decisions. Experts are mostly the humans involved in a loop, but when algorithms encounter problems, citizens are also involved at several stages. In this paper, we present three main examples of citizens-in-the-loop: (a) when algorithms provide incorrect suggestions; (b) when algorithms fail to know how to perform classification; and (c) when algorithms pose queries. We consider the implications of the emphasis on optimization on the ideal of science and the role of citizen scientists from a perspective informed by Science and Technology Studies (STS) and Information Systems (IS). Based on our findings, we conclude that ML in CS classification projects, far from being deterministic in its nature and effects, may be open to question. There is no guarantee that these technologies can replace citizen scientists, nor any guarantee that they can provide citizens with opportunities for more interesting tasks.

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Machine Learning in Citizen Science: Promises and Implications

Cited by 22 publications

References 32 publications

TeaTime4Schools: Using Data Mining Techniques to Model Litter Decomposition in Austrian Urban School Soils

TeaTime4Schools: Using Data Mining Techniques to Model Litter Decomposition in Austrian Urban School Soils

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

Narratives of epistemic agency in citizen science classification projects: ideals of science and roles of citizens

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