Machine Learning in Citizen Science: Promises and Implications

FRANZEN, Martina, Laure KLOETZER, Marisa PONTI, Jakub TROJAN a Julián VICENS. Machine Learning in Citizen Science: Promises and Implications. In Katrin Vohland, Anne Land-Zandstra, Luigi Ceccaroni, Rob Lemmens, Josep Perelló, Marisa Ponti, Roeland Samson, Katherin Wagenknecht. The Science of Citizen Science. Cham: Springer, 2021, s. 183-198. ISBN 978-3-030-58277-7. Dostupné z: https://dx.doi.org/10.1007/978-3-030-58278-4.

Základní údaje

• Originální název: Machine Learning in Citizen Science: Promises and Implications
• Název česky: Strojové učení v občanské vědě: přísliby a implikace
• Autoři: FRANZEN, Martina, Laure KLOETZER, Marisa PONTI, Jakub TROJAN a Julián VICENS.
• Vydání: Cham, The Science of Citizen Science, od s. 183-198, 16 s. 2021.
• Nakladatel: Springer

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Kapitola resp. kapitoly v odborné knize
• Stát vydavatele: Německo
• Utajení: není předmětem státního či obchodního tajemství
• Forma vydání: tištěná verze "print"
• WWW: Open Access
• Organizační jednotka: Přírodovědecká fakulta
• ISBN: 978-3-030-58277-7
• Doi: http://dx.doi.org/10.1007/978-3-030-58278-4
• Klíčová slova anglicky: Algorithms; Artificial intelligence; Computer vision; Machine learning; Transparency; Sensor; Datafication
• Štítky: RIV ne, topvydavatel
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

The chapter gives an account of both opportunities and challenges of human–machine collaboration in citizen science. In the age of big data, scientists are facing the overwhelming task of analysing massive amounts of data, and machine learning techniques are becoming a possible solution. Human and artificial intelligence can be recombined in citizen science in numerous ways. For example, citizen scientists can be involved in training machine learning algorithms in such a way that they perform certain tasks such as image recognition. To illustrate the possible applications in different areas, we discuss example projects of human–machine cooperation with regard to their underlying concepts of learning. The use of machine learning techniques creates lots of opportunities, such as reducing the time of classification and scaling expert decision-making to large data sets. However, algorithms often remain black boxes and data biases are not visible at first glance. Addressing the lack of transparency both in terms of machine action and in handling user-generated data, the chapter discusses how machine learning is actually compatible with the idea of active citizenship and what conditions need to be met in order to move forward – both in citizen science and beyond.