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.