BEST PAPER NOMINATION
Answering complex questions such as “How do I grow better tomatoes?” often requires individuals to conduct extensive online research and synthesis. Can we crowdsource this complex, high context process with 100 crowdworkers conducting microtasks distributedly? The Knowledge Accelerator uses crowdworkers to extract and synthesize text clips across web pages into coherent articles without a centralized coordinator.

Abstract

Crowdsourcing offers a powerful new paradigm for onlinework. However, real world tasks are often interdependent,requiring a big picture view of the difference pieces involved. Existing crowdsourcing approaches that support such tasks – ranging from Wikipedia to flash teams – are bottleneckedby relying on a small number of individuals to maintain thebig picture. In this paper, we explore the idea that a computational system can scaffold an emerging interdependent,big picture view entirely through the small contributions ofindividuals, each of whom sees only a part of the whole. Toinvestigate the viability, strengths, and weaknesses of this approach we instantiate the idea in a prototype system for accomplishing distributed information synthesis and evaluateits output across a variety of topics. We also contribute a setof design patterns that may be informative for other systemsaimed at supporting big picture thinking in small pieces.

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Media Coverage

• Pittsburgh Post-Gazette: Crowdsourcing work? Get rid of the human supervisor
• Campus Technology: Research Project Mixes Humans and Machines for Better Crowdsourcing
• Neuraoscience News: Crowd Augmented Cognition: Combining Human and Machine Intelligence to Accelerate Learning
• DZone: Research Suggests AI Managers Effective for Crowdsourcing
• PhysOrg: Crowd-augmented cognition: Team develops tools that combine human and machine intelligence to accelerate learning
• TechExplore: Big thinking in small pieces: Computer guides humans in crowdsourced research
• Spend Matters: Crowdsourcing and Cognitive Computing: Are You Ready for the Future of Work?
• Science Daily: Crowd-augmented cognition - combine human, machine intelligence to accelerate learning
• EurekAlert: Big thinking in small pieces: Computer guides humans in crowdsourced research
• EurekAlert: Crowd-augmented cognition
• NSF News: Big thinking in small pieces: Computer guides humans in crowdsourced research
• NSF News: Crowd-augmented cognition
• CMU SCS News: Big thinking in small pieces: Computer guides humans in crowdsourced research
• CMU HCII News: Computer Guides Humans in Crowdsourced Research

Citation

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Nathan Hahn, Joseph Chang, Ji Eun Kim, and Aniket Kittur. 2016.
The Knowledge Accelerator: Big Picture Thinking in Small Pieces.
In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16).
ACM, New York, NY, USA, 2258-2270. DOI: http://dx.doi.org/10.1145/2858036.2858364

Bibtex

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@inproceedings{Hahn:2016:KAB:2858036.2858364,
 author = {Hahn, Nathan and Chang, Joseph and Kim, Ji Eun and Kittur, Aniket},
 title = {The Knowledge Accelerator: Big Picture Thinking in Small Pieces},
 booktitle = {Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems},
 series = {CHI '16},
 year = {2016},
 isbn = {978-1-4503-3362-7},
 location = {Santa Clara, California, USA},
 pages = {2258--2270},
 numpages = {13},
 url = {http://doi.acm.org/10.1145/2858036.2858364},
 doi = {10.1145/2858036.2858364},
 acmid = {2858364},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {complex workflow, crowd work, crowdsourcing, design patterns, information synthesis},
}