arXiv:2606.04326v1 Announce Type: new Abstract: Concept bottleneck models predict outcomes from high-level concepts detected in inputs. Although concepts provide a simple way to reap benefits from interpretability, very few datasets include concept labels. This limits researchers' ability to determine which problems are suitable for these models, isolate the factors that drive their performance or lead to failures, or uncover which algorithms perform well. In this paper, we develop synthetic benchmarks for concept-bottleneck models, focusing on their two main use cases: decision support, in wh
The increasing complexity and opacity of AI models necessitate better interpretability, making concept bottleneck models a crucial area of research that currently lacks robust evaluation tools.
This development provides a foundational tool for researchers and developers to rigorously evaluate and improve interpretable AI models, directly impacting their reliability and applicability in critical domains.
The availability of synthetic benchmarks will allow for more targeted development and understanding of concept bottleneck models, enabling clearer insights into their performance drivers and limitations.
- · AI researchers
- · AI developers
- · Companies using interpretable AI
- · Developers of uninterpretable AI models (comparatively)
Improved evaluation and faster development cycles for interpretable AI models.
Increased adoption of concept bottleneck models in applications requiring transparency and reliability, such as decision support systems.
Higher public trust and regulatory acceptance for AI systems due to enhanced interpretability, potentially accelerating AI integration into sensitive sectors.
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