We seek contributions from all areas of machine learning that help to make human computation and crowdsourcing systems more efficient, robust, scalable, and/or lead to a better understanding of such systems. More specifically, the workshop invites applications of machine learning that advance our understanding in the following areas of crowdsourcing:
- Efficient and robust aggregation of opinions or knowledge
- Automatic quality control and verification schemes for user generated content
- Task-specific incentive design that inhibits collusion or cheating
- Analysis and modeling of interactions among humans
- Analysis of individual and aggregate decision making
We also encourage analysis and comparisons of various machine learning approaches for specific application scenarios in human computation and crowdsourcing systems.
Workshop on Machine Learning in Human Computation & Crowdsourcing 