Can't we all just get along? Citizen scientists interacting with algorithms


  • Marisa Ponti University of Gothenburg
  • Laure Kloetzer‬ University of Neuchâtel
  • Grant Miller Zooniverse
  • Frank O. Ostermann University of Twente
  • Sven Schade European Commission - Joint Research Centre (JRC)



  Responding to the continued and accelerating rise of Machine Learning (ML) in citizen science, we organized a discussion panel at the 3rd European Citizen Science 2020 Conference to initiate a dialogue on how citizen scientists interact and collaborate with algorithms. This brief summarizes a presentation about two Zooniverse projects which illustrated the impact that new developments in ML are having on citizen science projects which involve visual inspection of large datasets. We also share the results of a poll to elicit opinions and ideas from the audience on two statements, one positive and one critical of using ML in CS. The discussion with the participants raised several issues that we grouped into four main themes: a) democracy and participation; b) skill-biased technological change; c) data ownership vs public domain/digital commons, and d) transparency. All these issues warrant further research for those who are concerned about ML in citizen science.  




How to Cite

Ponti, M., Kloetzer‬, L., Miller, G., Ostermann, F. O., & Schade, S. (2021). Can’t we all just get along? Citizen scientists interacting with algorithms. Human Computation, 8(2), 5-14.