The emergence of human computation systems, including
Mechanical Turk and games with a purpose, has made it feasible
to distribute relevance judgment tasks to workers over the Web.
Most human computation systems assign tasks to individuals
randomly, and such assignments may match workers with tasks
that they may be unqualified or unmotivated to perform. We
compare two groups of workers, those given a choice of queries to
judge versus those who are not, in terms of their self-rated
competence and their actual performance. Results show that
when given a choice of task, workers choose ones for which they
have greater expertise, interests, confidence, and understanding.