Research map / Label-efficient and robust learning / Recognition and long-tail

On the Concept Trustworthiness in Concept Bottleneck Models

AAAI 2024 · Label-efficient and robust learning › Recognition and long-tail · approach: Concept bottleneck models · 10 citations (OpenAlex)

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The paper examines concept trustworthiness in Concept Bottleneck Models, where the input-to-concept mapping is a black box, and proposes a method to make intermediate concepts trustworthy.

Problem

Concept Bottleneck Models offer interpretable concept-to-label prediction, but the input-to-concept mapping remains a black box, so the predicted concepts may not be trustworthy.

Approach

Insufficient information: the provided text is only the AAAI formatting template, not the paper body, so the specific method is not recoverable from the text given.

Limitations

• Full paper body not provided; only the AAAI template and abstract fragment are available

Experimental setup

Builds on: concept bottleneck models

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