On the Improvement of Quality and Reliability of Trust Cues in Micro-task Crowdsourcing

Abstract

Micro-task crowdsourcing has become a successful mean to obtain high-quality data from a large crowd of diverse people. In this context, trust between all the involved actors (i.e. requesters, workers, and platform owners) is a critical factor for acceptance and long-term success. As actors have no expectation for “real life” meetings, thus trust can only be attributed through computer-mediated trust cues like workers qualifications and requester ratings. Such cues are often the result of technical or social assessments that are performed in isolation, considering only a subset of relevant properties, and with asynchronous and asymmetrical interactions. In this paper, we advocate for a new generation of micro-task crowdsourcing systems that pursue an holistic understanding of trust, by offering an open, transparent, privacy-friendly, and socially-aware view on the all the actors of a micro-task crowdsourcing environment.