• Corpus ID: 233393922

Weakly Supervised Multi-task Learning for Concept-based Explainability

@article{Belem2021WeaklySM,
  title={Weakly Supervised Multi-task Learning for Concept-based Explainability},
  author={Catarina Bel'em and Vladimir Balayan and Pedro Saleiro and P. Bizarro},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.12459}
}

• Catarina Bel'em, Vladimir Balayan, +1 author P. Bizarro
• Published 26 April 2021
• Computer Science
• ArXiv

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.