  In ML-aided decision-making tasks, such as fraud detection or medical
diagnosis, the human-in-the-loop, usually a domain-expert without technical ML
knowledge, prefers high-level concept-based explanations instead of low-level
explanations based on model features. To obtain faithful concept-based
explanations, we leverage multi-task learning to train a neural network that
jointly learns to predict a decision task based on the predictions of a
precedent explainability task (i.e., multi-label concepts). There are two main
challenges to overcome: concept label scarcity and the joint learning. To
address both, we propose to: i) use expert rules to generate a large dataset of
noisy concept labels, and ii) apply two distinct multi-task learning strategies
combining noisy and golden labels. We compare these strategies with a fully
supervised approach in a real-world fraud detection application with few golden
labels available for the explainability task. With improvements of 9.26% and of
417.8% at the explainability and decision tasks, respectively, our results show
it is possible to improve performance at both tasks by combining labels of
heterogeneous quality.
