Leakage and Interpretability in Concept-Based Models
📰 ArXiv cs.AI
Researchers propose a framework to quantify information leakage in concept-based models, which can improve interpretability in high-risk scenarios
Action Steps
- Identify high-level intermediate concepts in concept-based models
- Quantify information leakage using the proposed information-theoretic framework
- Analyze the trade-off between interpretability and leakage in model development
- Apply the framework to real-world high-risk scenarios to evaluate model reliability
Who Needs to Know This
AI engineers and ML researchers can benefit from this framework to develop more reliable and interpretable models, while data scientists can apply it to identify potential leakage in their models
Key Insight
💡 Information leakage can occur in concept-based models, compromising their interpretability and reliability
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🚨 Information leakage in concept-based models can compromise interpretability! 💡 New framework to quantify and mitigate leakage
Key Takeaways
Researchers propose a framework to quantify information leakage in concept-based models, which can improve interpretability in high-risk scenarios
Full Article
Title: Leakage and Interpretability in Concept-Based Models
Abstract:
arXiv:2504.14094v3 Announce Type: replace-cross Abstract: Concept-based Models aim to improve interpretability by predicting high-level intermediate concepts, representing a promising approach for deployment in high-risk scenarios. However, they are known to suffer from information leakage, whereby models exploit unintended information encoded within the learned concepts. We introduce an information-theoretic framework to rigorously characterise and quantify leakage, and define two complementary
Abstract:
arXiv:2504.14094v3 Announce Type: replace-cross Abstract: Concept-based Models aim to improve interpretability by predicting high-level intermediate concepts, representing a promising approach for deployment in high-risk scenarios. However, they are known to suffer from information leakage, whereby models exploit unintended information encoded within the learned concepts. We introduce an information-theoretic framework to rigorously characterise and quantify leakage, and define two complementary
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