Weakly Supervised Multi-task Learning for Concept-based Explainability
- URL: http://arxiv.org/abs/2104.12459v1
- Date: Mon, 26 Apr 2021 10:42:19 GMT
- Title: Weakly Supervised Multi-task Learning for Concept-based Explainability
- Authors: Catarina Bel\'em, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro
- Abstract summary: We leverage multi-task learning to train a neural network that jointly learns to predict a decision task.
There are two main challenges to overcome: concept label scarcity and the joint learning.
We show it is possible to improve performance at both tasks by combining labels of heterogeneous quality.
- Score: 3.441021278275805
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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