Related papers: Detecting adversaries in Crowdsourcing

Detecting adversaries in Crowdsourcing

URL: http://arxiv.org/abs/2110.04117v1
Date: Thu, 7 Oct 2021 15:07:07 GMT
Title: Detecting adversaries in Crowdsourcing
Authors: Panagiotis A. Traganitis and Georgios B. Giannakis
Abstract summary: 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. 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.
Score: 71.20185379303479
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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