# Diverse Concept Proposals for Concept Bottleneck Models

arXiv: https://arxiv.org/abs/2412.18059 · HTML: https://mlsanity.com/paper/2412.18059/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [On Completeness-aware Concept-Based Explanations in Deep Neural Networks](https://mlsanity.com/paper/1910.07969/) (2019)
- [Learning Qualitatively Diverse and Interpretable Rules for Classification](https://mlsanity.com/paper/1806.08716/) (2018)
- [TensorFlow: A system for large-scale machine learning](https://mlsanity.com/paper/1605.08695/) (2016)

## Abstract (arXiv)

Concept bottleneck models are interpretable predictive models that are often used in domains where model trust is a key priority, such as healthcare. They identify a small number of human-interpretable concepts in the data, which they then use to make predictions. Learning relevant concepts from data proves to be a challenging task. The most predictive concepts may not align with expert intuition, thus, failing interpretability with no recourse. Our proposed approach identifies a number of predictive concepts that explain the data. By offering multiple alternative explanations, we allow the human expert to choose the one that best aligns with their expectation. To demonstrate our method, we show that it is able discover all possible concept representations on a synthetic dataset. On EHR data, our model was able to identify 4 out of the 5 pre-defined concepts without supervision.