Citizen science has proved to be a unique and effective tool in helping science and society cope with the ever-growing data rates and volumes that characterize the modern research landscape. It also serves a critical role in engaging the public with research in a direct, authentic fashion and by doing so promotes a better understanding of the processes of science. To take full advantage of the onslaught of data being experienced across the disciplines, it is essential that citizen science platforms leverage the complementary strengths of humans and machines. This Perspectives piece explores the issues encountered in designing human-machine systems optimized for both efficiency and volunteer engagement, while striving to safeguard and encourage opportunities for serendipitous discovery. We discuss case studies from Zooniverse, a large online citizen science platform, and show that combining human and machine classifications can efficiently produce results superior to those of either one alone and how smart task allocation can lead to further efficiencies in the system. While these examples make clear the promise of human-machine integration within an online citizen science system, we then explore in detail how system design choices can inadvertently lower volunteer engagement, create exclusionary practices, and reduce opportunity for serendipitous discovery. Throughout we investigate the tensions that arise when designing a human-machine system serving the dual goals of carrying out research in the most efficient manner possible while empowering a broad community to authentically engage in this research.
|Original language||English (US)|
|Number of pages||8|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - Feb 5 2019|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS. We gratefully acknowledge the tremendous efforts of the Zooniverse web development team, the research teams leading each Zooniverse project, and the worldwide community of Zooniverse volunteers who make this all possible. This publication uses data generated via the Zooniverse.org platform, development of which is funded by generous support, including a Global Impact Award from Google, and by a grant from the Alfred P. Sloan Foundation. We also acknowledge funding in part for several of the human–machine studies from the National Science Foundation Awards IIS-1619177, IIS-1619071, and IIS-1547880.
- Biological sciences
- Citizen science
- Human computing interaction
- Machine learning
- Physical sciences
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't
- Research Support, U.S. Gov't, Non-P.H.S.