@article {2024,
	title = {Artificial Intelligence and the Future of Citizen Science},
	journal = {Citizen Science: Theory and Practice},
	volume = {9},
	year = {2024},
	month = {11/2024},
	pages = {32},
	doi = {10.5334/cstp.812},
	author = {Fortson, Lucy and Crowston, Kevin and Kloetzer, Laure and Ponti, Marisa}
}
@proceedings {bullard2024,
	title = {Folksonomies in crowdsourcing platforms: Three tensions associated with the development of shared language in distributed groups},
	year = {2024},
	abstract = {<p>Members of cooperative groups can work together more effectively if they develop a shared language, but distributed groups face barriers to doing so. To better understand how shared language can emerge in and support the work of distributed groups, we review the literature on folksonomies (a kind of shared language) in crowdsourcing systems (one type of distributed work). The review highlights three tensions associated with the development of folksonomies in crowdsourcing. The first problem is who has the power to decide on adopted terminology. Second, different users of the language may have different needs. There might be tension if people tagging objects are not the same as those using these tags to search for content. Finally, projects need to decide when to intervene to maintain a balance between a stable ontology and the ability of the project to accommodate ongoing changes. We illustrate these considerations by comparing how they are handled in the story-sharing site Archive of Our Own, the citizen-science project GravitySpy, and the photo-sharing site Flickr.</p>},
	doi = {10.48340/ecscw2024_n06},
	attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/ECSCW2024_Folksonomy_Crowdsourcing__Final_.pdf},
	author = {Bullard, Julia and Crowston, Kevin and Jackson, Corey B. and Smith, Alexander O. and {\O}sterlund, Carsten}
}
@conference {osterlund2024,
	title = {Mutual learning in human-AI interaction},
	booktitle = {Trust and Reliance in Evolving Human-AI Workflows (TREW) Workshop, ACM CHI Conference},
	year = {2024},
	address = {Honolulu, HI},
	abstract = {<p>We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the Zone of Proximal Development concept, which allows us to describe the augmentation of human learning by AI, human augmentation of machine learning and how tasks can be designed to facilitate co-augmentation. Methodologically, the study utilizes a design-science approach to explore the design, deployment, and evaluations of the Gravity Spy citizen science project. The findings highlight the challenges and opportunities of co-augmentation, where both humans and machines contribute to each other{\textquoteright}s learning and capabilities.\&nbsp; The research contributes to the existing literature by emphasizing the role of ZPD in citizen science projects, showcasing how the concept supports ongoing learning for volunteers and keeps machine learning aligned with evolving data.</p>},
	attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/TREW_Workshop_Paper.pdf},
	author = {{\O}sterlund, Carsten and Crowston, Kevin and Jackson, Casey B. and Takou-Ayaoh, Marlene and Wu, Yunan and Katsaggelos, Aggelos K.}
}
