Artificial Intelligence (AI) systems often depend on information provided by multiple agents (human or otherwise), for example sensor data, crowdsourced human computation, or human trajectory inputs for inverse reinforcement learning. However, eliciting accurate data can be costly, either due to the effort invested in obtaining it, as in crowdsourcing, or due to the needed maintenance of automated systems, as in distributed sensor systems. Low quality data not only degrades the performance of AI systems, but may also pose safety concerns. Thus, it becomes important to verify the correctness of data and be smart in how data is aggregated, and to provide incentives to promote effort and high-quality data. The aim of the workshop is to encourage discussions and contributions on the following aspects: