Mapping Citizen Science through the Lens of Human-Centered AI.

Janet Rafner, Miroslav Gajdacz, Gitte Kragh, Arthur Hjorth, Anna Gander, Blanka Palfi, Aleksandra Berditchevskiaia, François Grey, Kobi Gal, Avi Segal, Mike Wamsley, Josh Aaron Miller, Dominik Dellermann, Mordechai Haklay, Pietro Michelucci, Jacob Friis Sherson

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number1
Pages (from-to)66-95
Number of pages30
JournalHuman Computation
Issue number1
StatePublished - 16 Nov 2022


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