arXiv:2603.01372v2 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of end-to-end neural networks by introducing a layer of concepts and predicting the class label from the concept predictions. A key property of CBMs is that they support interventions, i.e., domain experts can correct mispredicted concept values at test time to improve the final accuracy. However, typical CBMs apply interventions by overwriting only the corrected concept while leaving other concept predictions unchanged, which ignores causal dependencies among concepts. To address
This research continues the ongoing effort within AI to improve transparency and trustworthiness, particularly as models become more complex and are deployed in sensitive applications.
Improving the interpretability and intervenability of AI models is crucial for their adoption in high-stakes domains, allowing human experts to understand and correct their reasoning.
This paper proposes a method to incorporate causal dependencies among concepts in Concept Bottleneck Models, enabling more effective human intervention by addressing the limitations of prior approaches.
- · AI researchers
- · Industries requiring explainable AI
- · Domain experts using AI systems
- · Black-box AI models
- · Systems with limited human oversight
AI models gain improved interpretability and allow for more sophisticated human intervention.
Increased trust and adoption of advanced AI systems in critical decision-making processes.
New regulatory frameworks may emerge that mandate AI models to demonstrate causal interpretability and intervenability.
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