LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems-Reference-Cited by-同舟云学术

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LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems

Published:2024-05-11 Issue: Volume:35 Page:1-21
ISSN:
Container-title:Proceedings of the CHI Conference on Human Factors in Computing Systems
language:
Short-container-title:

Author:

Li Chu¹^ORCID,Zhang Zhihan¹^ORCID,Saugstad Michael¹^ORCID,Safranchik Esteban¹^ORCID,Kulkarni Chaitanyashareef¹^ORCID,Huang Xiaoyu²^ORCID,Patel Shwetak¹^ORCID,Iyer Vikram³^ORCID,Althoff Tim¹^ORCID,Froehlich Jon E.¹^ORCID

Affiliation:

1. Paul G. Allen School of Computer Science & Engineering, University of Washington, United States

2. Department of Mechanical Engineering, University of California, Berkeley, United States

3. Paul G. Allen School of Computer Science and Engineering, University of Washington, United States

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3613904.3642089

Reference100 articles.

1. Guidelines for Human-AI Interaction

2. John Robert Anderson, C. Franklin Boyle, Robert Farrell, and Brian J. Reiser. 1984. Cognitive principles in the design of computer tutors. Department of Psychology, Carnegie-Mellon University.

3. Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance

4. Tanya Y. Berger-Wolf, Daniel I. Rubenstein, Charles V. Stewart, Jason A. Holmberg, Jason Parham, Sreejith Menon, Jonathan Crall, Jon Van Oast, Emre Kiciman, and Lucas Joppa. 2017. Wildbook: Crowdsourcing, computer vision, and data science for conservation. arXiv preprint arXiv:1710.08880 (2017).

5. Soylent

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