  Machine learning (ML) solutions are prevalent. However, many challenges exist
in making these solutions business-grade. One major challenge is to ensure that
the ML solution provides its expected business value. In order to do that, one
has to bridge the gap between the way ML model performance is measured and the
solution requirements. In previous work (Barash et al, "Bridging the gap...")
we demonstrated the effectiveness of utilizing feature models in bridging this
gap. Whereas ML performance metrics, such as the accuracy or F1-score of a
classifier, typically measure the average ML performance, feature models shed
light on explainable data slices that are too far from that average, and
therefore might indicate unsatisfied requirements. For example, the overall
accuracy of a bank text terms classifier may be very high, say $98\% \pm 2\%$,
yet it might perform poorly for terms that include short descriptions and
originate from commercial accounts. A business requirement, which may be
implicit in the training data, may be to perform well regardless of the type of
account and length of the description. Therefore, the under-performing data
slice that includes short descriptions and commercial accounts suggests
poorly-met requirements. In this paper we show the feasibility of automatically
extracting feature models that result in explainable data slices over which the
ML solution under-performs. Our novel technique, IBM FreaAI aka FreaAI,
extracts such slices from structured ML test data or any other labeled data. We
demonstrate that FreaAI can automatically produce explainable and
statistically-significant data slices over seven open datasets.
