  Crowdsourcing has become an important tool to collect data for various
artificial intelligence applications and auction can be an effective way to
allocate work and determine reward in a crowdsourcing platform. In this paper,
we focus on the crowdsourcing of small tasks such as image labelling and voice
recording where we face a number of challenges. First, workers have different
limits on the amount of work they would be willing to do, and they may also
misreport these limits in their bid for work. Secondly, if the auction is
repeated over time, unsuccessful workers may drop out of the system, reducing
competition and diversity. To tackle these issues, we first extend the results
of the celebrated Myerson's optimal auction mechanism for a single-parameter
bid to the case where the bid consists of the unit cost of work, the maximum
amount of work one is willing to do, and the actual work completed. We show
that a simple payment mechanism is sufficient to ensure a dominant strategy
from the workers, and that this dominant strategy is robust to the true utility
function of the workers. Secondly, we propose a novel, flexible work allocation
mechanism, which allows the requester to balance between cost efficiency and
equality. While cost minimization is obviously important, encouraging equality
in the allocation of work increases the diversity of the workforce as well as
promotes long-term participation on the crowdsourcing platform. Our main
results are proved analytically and validated through simulations.
