Mapping Citizen Science through the Lens of Human-Centered AI

Authors

  • Janet Rafner Center for Hybrid Intelligence, Department of Management, School of Business and Social Sciences, Aarhus University,
  • Miroslav Gajdacz Center for Hybrid Intelligence, Department of Management, School of Business and Social Sciences, Aarhus University
  • Gitte Kragh Center for Hybrid Intelligence, Department of Management, School of Business and Social Sciences, Aarhus University
  • Arthur Hjorth Center for Hybrid Intelligence, Department of Management, School of Business and Social Sciences, Aarhus University
  • Anna Gander Department of Applied Information Technology, University of Gothenburg
  • Blanka Palfi Center for Hybrid Intelligence, Department of Management, School of Business and Social Sciences, Aarhus University
  • Aleksandra Berditchevskiaia NESTA
  • Francois Grey University of Geneva
  • Kobi Gal Ben-Gurion University
  • Avi Segal Ben-Gurion University
  • Mike Wamsley University of Manchester
  • Joshua Miller Northeastern
  • Dominik Dellermann Vencortex
  • Mordechai Haklay Geographical Information Science, University College London
  • Pietro Michelucci Human Computation Institute
  • Jacob Sherson Center for Hybrid Intelligence, Department of Management, School of Business and Social Sciences, Aarhus University

DOI:

https://doi.org/10.15346/hc.v9i1.133

Abstract

Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value human agency alongside productivity, we discuss and categorize the potential of solving Citizen Science (CS) tasks with Hybrid Intelligence (HI), a synergetic mixture of human and artificial intelligence. Due to the unique participant-centered set of values and the abundance of tasks drawing upon both human common sense and complex 21st century skills, we believe that the field of CS offers an invaluable testbed for the development of human-centered AI including HI, while also benefiting CS. In order to investigate this potential, we first relate CS to adjacent computational disciplines. Then, we demonstrate that CS projects can be grouped according to their potential for HI-enhancement by examining two key dimensions: the level of digitization and the amount of knowledge or experience required for participation. Finally, we propose a framework for types of human-AI interaction in CS based on established criteria of HI. This “HI lens” provides the CS community with an overview of ways to utilize the combination of AI and human intelligence in their projects. For AI researchers, this work highlights the opportunity CS presents to engage with real-world data sets and explore new AI methods and applications.

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Published

2022-11-16

How to Cite

Rafner, J., Gajdacz, M., Kragh, G., Hjorth, A., Gander, A. ., Palfi, B., Berditchevskiaia, A., Grey, F., Gal, K., Segal, A. ., Wamsley, M., Miller, J., Dellermann, D. ., Haklay, M., Michelucci, P., & Sherson, J. (2022). Mapping Citizen Science through the Lens of Human-Centered AI. Human Computation, 9(1), 66-95. https://doi.org/10.15346/hc.v9i1.133