Detecting adversaries in Crowdsourcing
This addresses security issues in crowdsourcing for machine learning practitioners, but it is incremental as it builds on existing models.
The paper tackles the problem of adversaries in crowdsourced classification under the Dawid and Skene model, developing an approach that uses second-order moments of annotator responses to identify and mitigate their impact, with empirical validation on synthetic and real datasets.
Despite its successes in various machine learning and data science tasks, crowdsourcing can be susceptible to attacks from dedicated adversaries. This work investigates the effects of adversaries on crowdsourced classification, under the popular Dawid and Skene model. The adversaries are allowed to deviate arbitrarily from the considered crowdsourcing model, and may potentially cooperate. To address this scenario, we develop an approach that leverages the structure of second-order moments of annotator responses, to identify large numbers of adversaries, and mitigate their impact on the crowdsourcing task. The potential of the proposed approach is empirically demonstrated on synthetic and real crowdsourcing datasets.