  A multitude of classifiers can be trained on the same data to achieve similar
performances during test time, while having learned significantly different
classification patterns. This phenomenon, which we call prediction
discrepancies, is often associated with the blind selection of one model
instead of another with similar performances. When making a choice, the machine
learning practitioner has no understanding on the differences between models,
their limits, where they agree and where they don't. But his/her choice will
result in concrete consequences for instances to be classified in the
discrepancy zone, since the final decision will be based on the selected
classification pattern. Besides the arbitrary nature of the result, a bad
choice could have further negative consequences such as loss of opportunity or
lack of fairness. This paper proposes to address this question by analyzing the
prediction discrepancies in a pool of best-performing models trained on the
same data. A model-agnostic algorithm, DIG, is proposed to capture and explain
discrepancies locally, to enable the practitioner to make the best educated
decision when selecting a model by anticipating its potential undesired
consequences. All the code to reproduce the experiments is available.
