  We describe Space Warps, a novel gravitational lens discovery service that
yields samples of high purity and completeness through crowd-sourced visual
inspection. Carefully produced colour composite images are displayed to
volunteers via a web- based classification interface, which records their
estimates of the positions of candidate lensed features. Images of simulated
lenses, as well as real images which lack lenses, are inserted into the image
stream at random intervals; this training set is used to give the volunteers
instantaneous feedback on their performance, as well as to calibrate a model of
the system that provides dynamical updates to the probability that a classified
image contains a lens. Low probability systems are retired from the site
periodically, concentrating the sample towards a set of lens candidates. Having
divided 160 square degrees of Canada-France-Hawaii Telescope Legacy Survey
(CFHTLS) imaging into some 430,000 overlapping 82 by 82 arcsecond tiles and
displaying them on the site, we were joined by around 37,000 volunteers who
contributed 11 million image classifications over the course of 8 months. This
Stage 1 search reduced the sample to 3381 images containing candidates; these
were then refined in Stage 2 to yield a sample that we expect to be over 90%
complete and 30% pure, based on our analysis of the volunteers performance on
training images. We comment on the scalability of the SpaceWarps system to the
wide field survey era, based on our projection that searches of 10$^5$ images
could be performed by a crowd of 10$^5$ volunteers in 6 days.
