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Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds As Organizations
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“Likely outcomes of the growing capability of learning algorithms include their ability to assemble high-functioning teams by matching expertise, the flagging of individuals who cause bottlenecks or are not working to expectation, the performance of quality control and task rerouting, and finally the dynamic reconfiguration of roles and workflows (Faraj et al., 2018; Valentine et al., 2017).”
From Working and organizing in the age of the learning algorithm (2018) · cites this paper as Valentine et al., 2017
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“No organizational form is perfect for all situations [52], and flash organizations are better suited for situations where these tradeoffs matter less.”
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Working and organizing in the age of the learning algorithm
Faraj, S., Pachidi, S., & Sayegh, K. (2018) · Information and Organization · doi:10.1016/j.infoandorg.2018.02.005 · Cited by 929
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?
Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300830 · Cited by 777
Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions
Alkhatib, A., & Bernstein, M. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300760 · Cited by 188
Self-citationWomen (Still) Ask For Less: Gender Differences in Hourly Rate in an Online Labor Marketplace
Foong, E., Vincent, N., Hecht, B., & Gerber, E. M. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274322 · Cited by 80
Whose AI Dream? In search of the aspiration in data annotation.
Wang, D., Prabhat, S., & Sambasivan, N. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502121 · Cited by 70
Metadata
- Authors
- Valentine, M. A., Retelny, D., To, A., Rahmati, N., Doshi, T., & Bernstein, M. S.
- Affiliations
- Stanford University, Stanford, CA, USA
- Carnegie Mellon University, Pittsburgh, PA, USA
- Stanford University, Palo Alto, CA, USA
- Venue
- Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2017-05-02
- Pages
- 3523-3537
- Cited by
- 123
- References
- 16
- Funders
- National Science Foundation (IIS-1351131)
- Stanford Interdisciplinary Graduate Fellowship
- Stanford Cyber Initiative
- Accenture Technology Labs
- Microsoft Research
- Stanford Institute for Research in the Social Sciences
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
