Mapping Citizen Science through the Lens of Human-Centered AI

Janet Frances Rafner; Miroslav Gajdacz; Gitte Kragh; Hermes Arthur Hjorth; Anna Gander; Blanka Sara Palfi; Aleksandra Berditchevskiaia; Francois Grey; Kobi Gal; Avi Segal; Mike Walmsley; Joshua Miller; Dominik Dellermann; Mordechai Haklay; Pietro Michelucci; Jacob Sherson

doi:10.15346/hc.v9i1.133

Mapping Citizen Science through the Lens of Human-Centered AI

Janet Frances Rafner, Miroslav Gajdacz, Gitte Kragh, Hermes Arthur Hjorth, Anna Gander, Blanka Sara Palfi, Aleksandra Berditchevskiaia, Francois Grey, Kobi Gal, Avi Segal, Mike Walmsley, Joshua Miller, Dominik Dellermann, Mordechai Haklay, Pietro Michelucci, Jacob Sherson

Department of Management

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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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.

Original language	English
Journal	Human Computation
Volume	9
Issue	1
Pages (from-to)	66-95
Number of pages	20
ISSN	2330-8001
DOIs	https://doi.org/10.15346/hc.v9i1.133
Publication status	Published - Nov 2022

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133-Article Text-766-2-10-20221118Final published version, 2.13 MBLicence: CC BY

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title = "Mapping Citizen Science through the Lens of Human-Centered AI",

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.",

author = "Rafner, {Janet Frances} and Miroslav Gajdacz and Gitte Kragh and Hjorth, {Hermes Arthur} and Anna Gander and Palfi, {Blanka Sara} and Aleksandra Berditchevskiaia and Francois Grey and Kobi Gal and Avi Segal and Mike Walmsley and Joshua Miller and Dominik Dellermann and Mordechai Haklay and Pietro Michelucci and Jacob Sherson",

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T1 - Mapping Citizen Science through the Lens of Human-Centered AI

AU - Rafner, Janet Frances

AU - Gajdacz, Miroslav

AU - Kragh, Gitte

AU - Hjorth, Hermes Arthur

AU - Gander, Anna

AU - Palfi, Blanka Sara

AU - Berditchevskiaia, Aleksandra

AU - Grey, Francois

AU - Gal, Kobi

AU - Segal, Avi

AU - Walmsley, Mike

AU - Miller, Joshua

AU - Dellermann, Dominik

AU - Haklay, Mordechai

AU - Michelucci, Pietro

AU - Sherson, Jacob

PY - 2022/11

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N2 - 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.

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

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