  Customers of machine learning systems demand accountability from the
companies employing these algorithms for various prediction tasks.
Accountability requires understanding of system limit and condition of
erroneous predictions, as customers are often interested in understanding the
incorrect predictions, and model developers are absorbed in finding methods
that can be used to get incremental improvements to an existing system.
Therefore, we propose an accountable error characterization method, AEC, to
understand when and where errors occur within the existing black-box models.
AEC, as constructed with human-understandable linguistic features, allows the
model developers to automatically identify the main sources of errors for a
given classification system. It can also be used to sample for the set of most
informative input points for a next round of training. We perform error
detection for a sentiment analysis task using AEC as a case study. Our results
on the sample sentiment task show that AEC is able to characterize erroneous
predictions into human understandable categories and also achieves promising
results on selecting erroneous samples when compared with the uncertainty-based
sampling.
