Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI
📰 ArXiv cs.AI
Learn a novel framework for robust post-hoc explainable AI that replaces input perturbation with model deactivation, improving explanation quality
Action Steps
- Read the Activation-Deactivation paper to understand the novel forward pass paradigm
- Implement the AD framework in your existing models to replace perturbation-based explainability methods
- Deactivate parts of the model corresponding to intended perturbations to generate explanations
- Compare the quality of explanations generated by AD with traditional perturbation-based methods
- Apply the AD framework to various models and datasets to test its robustness and generalizability
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this framework to improve the interpretability of their models, while researchers can use it to develop more robust explainability methods
Key Insight
💡 Replacing input perturbation with model deactivation can improve the quality of explanations in post-hoc explainable AI
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🚀 Introducing Activation-Deactivation, a novel framework for robust post-hoc explainable AI! 🤖
Key Takeaways
Learn a novel framework for robust post-hoc explainable AI that replaces input perturbation with model deactivation, improving explanation quality
Full Article
Title: Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI
Abstract:
arXiv:2510.01038v2 Announce Type: replace Abstract: Perturbation-based explainability methods face criticism due to their reliance on out-of-distribution mutants. This raises doubts about the quality of the explanations. In this paper, we introduce a novel forward pass paradigm, Activation-Deactivation (AD), which obviates the need for perturbation of the input. AD replaces perturbation of input features with switching off parts of the model corresponding to to the intended perturbations. We imp
Abstract:
arXiv:2510.01038v2 Announce Type: replace Abstract: Perturbation-based explainability methods face criticism due to their reliance on out-of-distribution mutants. This raises doubts about the quality of the explanations. In this paper, we introduce a novel forward pass paradigm, Activation-Deactivation (AD), which obviates the need for perturbation of the input. AD replaces perturbation of input features with switching off parts of the model corresponding to to the intended perturbations. We imp
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