Cyber - Project - Cyber Work: The Future of Networked Labor
Cyber Work: The Future of Networked Labor
Technology has transformed from a tool that supports work into a comprehensive infrastructure that connects workers to employers. Platforms such as Uber and Amazon Mechanical Turk, which announce themselves as the “gig economy” and “paid crowdsourcing”, signal a shift where workers and employers connect ad-hoc, at large scale, to accomplish complex tasks. This shift to online networked labor has the potential to dramatically reconfigure how we shape our careers, organizations, and market platforms, and in turn shifts how those careers, organizations and platforms shape our society. Inspired by this transformation and its risks, our project addresses challenges facing the entire span of the networked labor ecosystem: individuals, organizations, and the work platform itself. We study three fundamental questions: first, how will people manage their work lives online? Second, how might organizations look in a future of networked labor? Third, how do networked labor platforms succeed? To address these challenges, we propose a combination of social scientific, design and engineering endeavors. Our efforts aim to envision the future of digital work, and to inform and create the technological platforms that enable it.
Publications:
- Sharon Zhou, Melissa Valentine, Michael S. Bernstein. "In Search of the Dream Team: Temporally Constrained Multi-Armed Bandits for Identifying Effective Team Structures." CHI 2018
- Daniela Retelny, Melissa Valentine, Michael Bernstein. "No Workflow Can Ever Be Enough: How Crowdsourcing Workflows Constrain Complex Work." CSCW 2018
- Matt V. Leduc, Matthew O. Jackson, Ramesh Johari. “Pricing and Referrals in Diffusion Networks.” Coalition Theory Network 16.2017. http://www.coalitiontheory.net/content/pricing-and-referrals-diffusion-networks
- Daniela Retelny, Michael S. Bernstein, and Melissa A. Valentine. 2017. No Workflow Can Ever Be Enough: How Crowdsourcing Workflows Constrain Complex Work. Proc. ACM Hum.-Comput. Interact. 1, 2, Article 89 (November 2017), 23 pages. https://doi.org/10.1145/3134724
- Retelny, D., To, A., Rahmati, N., Doshi, T., Valentine, M.,Bernstein, M. “Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds As Organizations”, CHI 2017. Winner of Best Paper Award. http://confer.csail.mit.edu/chi2017/paper#!pn2790
- Michelony, A., Antonellis, I., & Johari, R. (2015). "Predicting bad job outcomes in online workplaces." Workshop on Crowdsourcing and Machine Learning (CrowdML). http://crowdml.cc/icml2015/
- Bernstein, M. et al. “Crowd Guilds: Worker-led Reputation and Feedback on Crowdsourcing Platforms.” CSCW 2017. https://arxiv.org/abs/1611.01572
- Hata, K. Krishna, R., Li, F., Bernstein, F. “A Glimpse Far into the Future: Understanding Long-term Crowd Worker Accuracy.” Semantic Scholar. 9/2016. https://pdfs.semanticscholar.org/d23b/1e9285d1d4b487e504d728d023d779d0ae65.pdf
- Gaikwad, N.N., et al. 2016. Boomerang: Rebounding the Consequences of Reputation Feedback on Crowdsourcing Platforms. In Proceedings of ACM Symposium on User Interface Software and Technology. ACM Press.
- Salehi, N., McCabe, A., Valentine, M., Bernstein, M. Huddle: Convening Stable and Familiar Crowd Teams Despite Unpredictable Availability. 10/2016. https://arxiv.org/abs/1610.08216
- Valentine, M. “Shared Responsibility and Coordination Behaviors in Temporary Teams.” 11/2016. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2872747