Measuring the (Un)Faithfulness of Concept-Based Explanations

1. 1Identify concept-based explanation methods (CBEMs) for deep vision models
2. 2Develop a surrogate model that combines concepts to compute the output
3. 3Evaluate the faithfulness of derived explanations by comparing them to the original model's internal computation
4. 4Analyze the results to improve model interpretability and trustworthiness

ML researchers and engineers benefit from understanding the faithfulness of concept-based explanations to improve model interpretability, while data scientists can apply these methods to various domains

💡 Faithfulness of concept-based explanations is crucial for trustworthy model interpretability

🤖 Measuring faithfulness of concept-based explanations in deep vision models 📊