  We study the problem of troubleshooting machine learning systems that rely on
analytical pipelines of distinct components. Understanding and fixing errors
that arise in such integrative systems is difficult as failures can occur at
multiple points in the execution workflow. Moreover, errors can propagate,
become amplified or be suppressed, making blame assignment difficult. We
propose a human-in-the-loop methodology which leverages human intellect for
troubleshooting system failures. The approach simulates potential component
fixes through human computation tasks and measures the expected improvements in
the holistic behavior of the system. The method provides guidance to designers
about how they can best improve the system. We demonstrate the effectiveness of
the approach on an automated image captioning system that has been pressed into
real-world use.
