  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.
