Towards learning to explain with concept bottleneck models: mitigating information leakage

• URL: http://arxiv.org/abs/2211.03656v1
• Date: Mon, 7 Nov 2022 16:10:36 GMT
• Title: Towards learning to explain with concept bottleneck models: mitigating information leakage
• Authors: Joshua Lockhart, Nicolas Marchesotti, Daniele Magazzeni, Manuela Veloso
• Abstract summary: Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint. A downstream model uses these predicted concept labels to predict the target label. The predicted concepts act as a rationale for the target prediction.
• Score: 19.52933192442871
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint. Then a downstream model uses these predicted concept labels to predict the target label. The predicted concepts act as a rationale for the target prediction. Model trust issues emerge in this paradigm when soft concept labels are used: it has previously been observed that extra information about the data distribution leaks into the concept predictions. In this work we show how Monte-Carlo Dropout can be used to attain soft concept predictions that do not contain leaked information.

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