  Teachable interfaces can empower end-users to attune machine learning systems
to their idiosyncratic characteristics and environment by explicitly providing
pertinent training examples. While facilitating control, their effectiveness
can be hindered by the lack of expertise or misconceptions. We investigate how
users may conceptualize, experience, and reflect on their engagement in machine
teaching by deploying a mobile teachable testbed in Amazon Mechanical Turk.
Using a performance-based payment scheme, Mechanical Turkers (N = 100) are
called to train, test, and re-train a robust recognition model in real-time
with a few snapshots taken in their environment. We find that participants
incorporate diversity in their examples drawing from parallels to how humans
recognize objects independent of size, viewpoint, location, and illumination.
Many of their misconceptions relate to consistency and model capabilities for
reasoning. With limited variation and edge cases in testing, the majority of
them do not change strategies on a second training attempt.
