Supervised learning from multiple experts: whom to trust when everyone lies a bit
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The social honeypot project: protecting online communities from spammers
Proceedings of the 19th international conference on World wide web
Quality management on Amazon Mechanical Turk
Proceedings of the ACM SIGKDD Workshop on Human Computation
Crowdsourcing systems on the World-Wide Web
Communications of the ACM
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As the volumes of AI problems involving human knowledge are likely to soar, crowdsourcing has become essential in a wide range of world-wide-web applications. One of the biggest challenges of crowdsourcing is aggregating the answers collected from crowd workers; and thus, many aggregate techniques have been proposed. However, given a new application, it is difficult for users to choose the best-suited technique as well as appropriate parameter values since each of these techniques has distinct performance characteristics depending on various factors (e.g. worker expertise, question difficulty). In this paper, we develop a benchmarking tool that allows to (i) simulate the crowd and (ii) evaluate aggregate techniques in different aspects (accuracy, sensitivity to spammers, etc.). We believe that this tool will be able to serve as a practical guideline for both researchers and software developers. While researchers can use our tool to assess existing or new techniques, developers can reuse its components to reduce the development complexity.