  AI systems can fail to learn important behaviors, leading to real-world
issues like safety concerns and biases. Discovering these systematic failures
often requires significant developer attention, from hypothesizing potential
edge cases to collecting evidence and validating patterns. To scale and
streamline this process, we introduce crowdsourced failure reports, end-user
descriptions of how or why a model failed, and show how developers can use them
to detect AI errors. We also design and implement Deblinder, a visual analytics
system for synthesizing failure reports that developers can use to discover and
validate systematic failures. In semi-structured interviews and think-aloud
studies with 10 AI practitioners, we explore the affordances of the Deblinder
system and the applicability of failure reports in real-world settings. Lastly,
we show how collecting additional data from the groups identified by developers
can improve model performance.
