How divergent is your data?
Eliana Pastor, Andrew Gavgavian, Elena Baralis, Luca de Alfaro · Proceedings of the VLDB Endowment · 2021
We present DivExplorer, a tool that enables users to explore datasets and find subgroups of data for which a classifier behaves in an anomalous manner. These subgroups, denoted as divergent subgroups, may exhibit, for example, higher-than-normal false positive or negative rates. DivExplorer can be used to analyze and debug classifiers. If the data has ethical or social implications, DivExplorer can be also used to identify bias in classifiers.