Machine Learning in Citizen Science: Promises and Implications

C 2021

Machine Learning in Citizen Science: Promises and Implications

FRANZEN, Martina, Laure KLOETZER, Marisa PONTI, Jakub TROJAN, Julián VICENS et. al.

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

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"

Odkazy

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

Změněno: 27. 1. 2021 12:22, RNDr. Jakub Trojan, MSc, Ph.D.

Anotace

V originále

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.

Přiložené soubory

2021_Book_TheScienceOfCitizenScience.pdf

Vyhledat podobné dokumenty

Citovat

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.

@inbook{1734498,
   author = {Franzen, Martina and Kloetzer, Laure and Ponti, Marisa and Trojan, Jakub and Vicens, Julián},
   address = {Cham},
   booktitle = {The Science of Citizen Science},
   doi = {http://dx.doi.org/10.1007/978-3-030-58278-4},
   editor = {Katrin Vohland, Anne Land-Zandstra, Luigi Ceccaroni, Rob Lemmens, Josep Perelló, Marisa Ponti, Roeland Samson, Katherin Wagenknecht},
   keywords = {Algorithms; Artificial intelligence; Computer vision; Machine learning; Transparency; Sensor; Datafication},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-030-58277-7},
   pages = {183-198},
   publisher = {Springer},
   title = {Machine Learning in Citizen Science: Promises and Implications},
   url = {https://link.springer.com/book/10.1007%2F978-3-030-58278-4},
   year = {2021}
}

TY  - CHAP
ID  - 1734498
AU  - Franzen, Martina - Kloetzer, Laure - Ponti, Marisa - Trojan, Jakub - Vicens, Julián
PY  - 2021
TI  - Machine Learning in Citizen Science: Promises and Implications
PB  - Springer
CY  - Cham
SN  - 9783030582777
KW  - Algorithms
KW  - Artificial intelligence
KW  - Computer vision
KW  - Machine learning
KW  - Transparency
KW  - Sensor
KW  - Datafication
UR  - https://link.springer.com/book/10.1007%2F978-3-030-58278-4
N2  - 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.
ER  -

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. \textit{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.

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