  Concept-based models perform prediction using a set of concepts that are
interpretable to stakeholders. However, such models often involve a fixed,
large number of concepts, which may place a substantial cognitive load on
stakeholders. We propose Selective COncept Models (SCOMs) which make
predictions using only a subset of concepts and can be customised by
stakeholders at test-time according to their preferences. We show that SCOMs
only require a fraction of the total concepts to achieve optimal accuracy on
multiple real-world datasets. Further, we collect and release a new dataset,
CUB-Sel, consisting of human concept set selections for 900 bird images from
the popular CUB dataset. Using CUB-Sel, we show that humans have unique
individual preferences for the choice of concepts they prefer to reason about,
and struggle to identify the most theoretically informative concepts. The
customisation and concept selection provided by SCOM improves the efficiency of
interpretation and intervention for stakeholders.
