Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large
groups of individuals toward solving problems. Common problems approached with …

With crowdsourcing systems, labels can be obtained with low cost, which facilitates the
creation of training sets for prediction model learning. However, the labels obtained from …

Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …

The term 'crowdsourcing'was initially introduced in 2006 to describe an emerging distributed
problem-solving model by online workers. Since then it has been widely studied and …

Collecting high quality conversational data can be very expensive for most applications and
infeasible for others due to privacy, ethical, or similar concerns. A promising direction to …

Citizens are increasingly becoming an important source of geographic information,
sometimes entering domains that had until recently been the exclusive realm of authoritative …

With the rise of artificial intelligence (AI), the issue of trust in AI emerges as a paramount
societal concern. Despite increased attention of researchers, the topic remains fragmented …

Background Although risk perception is a key predictor in health behavior theories, current
conceptions of risk comprise only one (deliberative) or two (deliberative vs …

To profit from crowdsourcing, organizations can engage in four different approaches:
microtasking, information pooling, broadcast search, and open collaboration. This article …

With the rapid growing of crowdsourcing systems, quite a few applications based on a
supervised learning paradigm can easily obtain massive labeled data at a relatively low …