The field of crowdsourcing and human computation has seen a steady invention of technology and new applications of technology to solve increasingly complex problems. We propose a new framework that serves as a complement to incremental innovation by assuming that some seemingly intractable social-scale problems can be solved, suggesting a crowd-ready process to subdivide the problem by parts and by phase and then seeking development of the needed technology. The framework outlines a technology-assisted human computation process which results in the participatory design of what we call a social problem-solving model—an actionable community story that enjoys the buy-in from the community because it is developed by the community. We propose reframing a given social problem in terms of a community-determined superordinate goal and then recursively subdividing obstacles to that goal into larger numbers of simpler obstacle subcomponents, which better lend themselves to community-proposed solutions. The question of resource matching to implement these solutions is handled as a separate step. Technology tools need to be developed to facilitate each phase of this process. We discuss potential beneficial characteristics of the problem-solving model designed by the community and the nature of the technology required to implement each phase of the process. Finally, we discuss limitations we foresee to this approach.

A Technology-Assisted Social Computing Framework for Solving Complex Social Problems

Blue Sky Session

technical paper

The 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2020) will be held October 26–28th virtually at The Netherlands Institute for Sound and Vision in Hilversum, The Netherlands.

While artificial intelligence (AI) and human-computer interaction (HCI) represent traditional mainstays of the conference, HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. We promote the exchange of advances in human computation and crowdsourcing not only among researchers, but also engineers and practitioners, to encourage dialogue across disciplines and communities of practice.

This year, we especially invite works that will generate new insights into the quality (or qualities) of human-annotated datasets, including elements such as reliability and replicability of human computation and crowdsourcing experiments, novel metrics for aggregation of results, and holistic approaches to deal with bias, fairness and interpretability.

HCOMP 2020

HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. 


Kevin Kells

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A Technology-Assisted Social Computing Framework for Solving Complex Social Problems

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