Climbing the Ladder of Interpretability with Counterfactual Concept
  Bottleneck Models
Gabriele Dominici Pietro Barbiero Francesco Giannini Martin Gjoreski Giuseppe Marra Marc Langheinrich
Current deep learning models are not designed to simultaneously address three
fundamental questions: predict class labels to solve a given classification
task (the "What?"), explain task predictions (the "Why?"), and imagine
alternative scenarios that could result in different predictions (the "What
if?"). The inability to answer these questions represents a crucial gap in
deploying reliable AI agents, calibrating human trust, and deepening
human-machine interaction. To bridge this gap, we introduce CounterFactual
Concept Bottleneck Models (CF-CBMs), a class of models designed to efficiently
address the above queries all at once without the need to run post-hoc
searches. Our results show that CF-CBMs produce: accurate predictions (the
"What?"), simple explanations for task predictions (the "Why?"), and
interpretable counterfactuals (the "What if?"). CF-CBMs can also sample or
estimate the most probable counterfactual to: (i) explain the effect of concept
interventions on tasks, (ii) show users how to get a desired class label, and
(iii) propose concept interventions via "task-driven" interventions.