@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}
}
@article {2024,
	title = {Editorial: Sharing work with AI: introduction to the special issue on the futures of work in the age of intelligent machines},
	journal = {Information Technology \& People},
	volume = {37},
	year = {2024},
	month = {Mar-12-2024},
	pages = {2353 - 2356},
	issn = {0959-3845},
	doi = {10.1108/ITP-12-2024-994},
	url = {https://www.emerald.com/insight/content/doi/10.1108/ITP-12-2024-994/full/htmlhttps://www.emerald.com/insight/content/doi/10.1108/ITP-12-2024-994/full/htmlhttps://www.emerald.com/insight/content/doi/10.1108/ITP-12-2024-994/full/xml},
	author = {Crowston, Kevin and Erickson, Ingrid and Nickerson, Jeffrey}
}
@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://waim.network/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.}
}
@conference {2024,
	title = {ReelFramer: Human-AI Co-Creation for News-to-Video Translation},
	booktitle = {CHI {\textquoteright}24: CHI Conference on Human Factors in Computing SystemsProceedings of the CHI Conference on Human Factors in Computing Systems},
	year = {2024},
	pages = {1 - 20},
	publisher = {ACM},
	organization = {ACM},
	address = {Honolulu HI USANew York, NY, USA},
	isbn = {9798400703300},
	doi = {10.1145/3613904.3642868},
	author = {Wang, Sitong and Menon, Samia and Long, Tao and Henderson, Keren and Li, Dingzeyu and Crowston, Kevin and Hansen, Mark and Nickerson, Jeffrey V and Chilton, Lydia B},
	editor = {Mueller, Florian Floyd and Kyburz, Penny and Williamson, Julie R. and Sas, Corina and Wilson, Max L. and Dugas, Phoebe Toups and Shklovski, Irina}
}
@proceedings {petridis2023anglekindling,
	title = {AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models},
	year = {2023},
	doi = {10.1145/3544548.3580907},
	url = {https://savvaspetridis.github.io/papers/anglekindling.pdf},
	author = {Petridis, Savvas and Diakopoulos, Nicholas and Crowston, Kevin and Hansen, Mark and Henderson, Keren and Jastrzebski, Stan and Nickerson, Jefrey V and Chilton, Lydia B}
}
@article {2021,
	title = {Hybrid intelligence in business networks},
	journal = {Electronic Markets},
	year = {2021},
	month = {Nov-06-2021},
	issn = {1019-6781},
	doi = {10.1007/s12525-021-00481-4},
	attachments = {https://waim.network/sites/crowston.syr.edu/files/Ebel2021_Article_HybridIntelligenceInBusinessNe.pdf},
	author = {Ebel, Philipp and S{\"o}llner, Matthias and Leimeister, Jan Marco and Crowston, Kevin and de Vreede, Gert-Jan}
}
