{"type":"ScholarlyArticle","title":"Quality Control Attack Schemes in Crowdsourcing","authors":[{"name":"Alessandro Checco","slug":"alessandro-checco","given":"Alessandro","family":"Checco","affiliation":"The University of Sheffield , UK"},{"name":"Jo Bates","slug":"jo-bates","given":"Jo","family":"Bates","affiliation":"The University of Sheffield , UK"},{"name":"Gianluca Demartini","slug":"gianluca-demartini","given":"Gianluca","family":"Demartini","affiliation":"The University of Queensland , Australia"}],"abstract":"An important precondition to build effective AI models is the collection of training data at scale. Crowdsourcing is a popular methodology to achieve this goal. Its adoption  introduces novel challenges in data quality control, to deal with under-performing and malicious annotators. One of the most popular quality assurance mechanisms, especially in paid micro-task crowdsourcing, is the use of a small set of pre-annotated tasks as gold standard, to assess in real time the annotators quality. In this paper, we highlight a set of vulnerabilities this scheme suffers: a group of colluding crowd workers can easily implement and deploy a decentralised machine learning inferential system to  detect and signal which parts of the task are more likely to be gold questions, making them ineffective as a quality control tool. Moreover, we demonstrate how the most common countermeasures against this attack are ineffective in practical scenarios. The basic architecture of the inferential system is composed of a browser plug-in and an external server where the colluding workers can share information. We implement and validate the attack scheme, by means of experiments on real-world data from a popular crowdsourcing platform.","doi":"10.24963/ijcai.2019/850","url":"https://ijcai.org/proceedings/2019/850","pdf":"https://ijcai.org/proceedings/2019/0850.pdf","markdown":"https://ijcai.org/proceedings/2019/850.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/850","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Best Sister Conferences","track":"Best Sister Conferences","number":850,"pages":{"first":6136,"last":6140},"keywords":["Machine Learning Applications: Applications of Unsupervised Learning","Humans and AI: Human Computation and Crowdsourcing","Machine Learning: Online Learning","Multidisciplinary Topics and Applications: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Benoit et al., 2016] Kenneth Benoit, Drew Conway, Ben- jamin E Lauderdale, Michael Laver, and Slava Mikhaylov. 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