Identifying Careless Workers in Crowdsourcing Platforms: A Game Theory Approach

Yashar Moshfeghi, Alvaro Francisco Huertas-Rosero, Joemon M. Jose

Abstract: In this paper we introduce a game scenario for crowdsourcing (CS) using incentives as a bait for careless (gambler) workers, who respond to them in a characteristic way. We hypothesise that careless workers are risk-inclined and can be detected in the game scenario by their use of time, and test this hypothesis in two steps: first, we formulate and prove a theorem stating that a risk-inclined worker will react to competition with shorter Task Completion Time (TCT) than a risk-neutral or risk-averse worker. Second, we check if the game scenario introduces a link between TCT and performance, by performing a crowdsourced evaluation using 35 topics from the TREC-8 collection. Experimental evidence confirms our hypothesis, showing that TCT can be used as a powerful discrimination factor to detect careless workers. This is a valuable result in the quest for quality assurance in CS-based micro tasks such as relevance assessment.