Department of Citizen Science, The Adler Planetarium, Chicago, IL 60605;
Center for Interdisciplinary Exploration and Research in Astrophysics, Northwestern University, Evanston, IL 60208.
Proc Natl Acad Sci U S A. 2019 Feb 5;116(6):1902-1909. doi: 10.1073/pnas.1807190116.
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 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.
公民科学已被证明是一种独特而有效的工具，可帮助科学和社会应对现代研究领域中日益增长的数据速率和数量。它还通过直接、真实的方式使公众参与研究，从而促进对科学过程的更好理解。为了充分利用各学科所面临的数据冲击，公民科学平台必须利用人类和机器的互补优势。本文探讨了在设计旨在提高效率和志愿者参与度的人机系统时遇到的问题，同时努力保护和鼓励偶然发现的机会。我们讨论了来自 Zooniverse 的案例研究，这是一个大型在线公民科学平台，展示了人类和机器分类的结合如何能够高效地产生优于单独使用任何一种方法的结果，以及智能任务分配如何导致系统进一步提高效率。虽然这些例子清楚地表明了在线公民科学系统中人机集成的前景，但我们随后详细探讨了系统设计选择如何无意中降低志愿者参与度、造成排斥性做法，并减少偶然发现的机会。我们一直在调查在设计一个既能以最有效的方式进行研究，又能使广大社区有能力真实参与研究的人机系统时出现的紧张局势。
