  Without well-labeled ground truth data, machine learning-based systems would
not be as ubiquitous as they are today, but these systems rely on substantial
amounts of correctly labeled data. Unfortunately, crowdsourced labeling is time
consuming and expensive. To address the concerns of effort and tedium, we
designed CAL, a novel interface to aid in data labeling. We made several key
design decisions for CAL, which include preventing inapt labels from being
selected, guiding users in selecting an appropriate label when they need
assistance, incorporating labeling documentation into the interface, and
providing an efficient means to view previous labels. We implemented a
production-quality implementation of CAL and report a user-study evaluation
that compares CAL to a standard spreadsheet. Key findings of our study include
users using CAL reported lower cognitive load, did not increase task time,
users rated CAL to be easier to use, and users preferred CAL over the
spreadsheet.
