ExplainabilityTransparencyFairnesscs.CV
Reliable or Deceptive? Investigating Gated Features for Smooth Visual Explanations in CNNs
Soham Mitra, Atri Sukul, Swalpa Kumar Roy, Pravendra Singh, Vinay Verma
April 30, 2024
arXiv.org
1 citations
Key Takeaways
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Visual explanation methods can be improved through technical refinements: The paper demonstrates that modifying how neural networks normalize and process activation layers (ScoreCAM++) produces better visual explanations of AI decisions than previous methods, suggesting that explanation quality depends significantly on implementation details.
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"Gating" lower-priority features may enhance reliability: By selectively filtering out less important activation signals, ScoreCAM++ appears to provide clearer, more focused explanations of what features the model actually used to make decisions, potentially reducing misleading or confusing explanations.
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Fairness in AI explanations matters: The authors specifically tested their method for "fairness in interpreting the decision-making process," indicating that responsible AI requires not just any explanation, but explanations that fairly and accurately represent how models work across different scenarios.
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Visual explanations warrant scrutiny: The paper's title raises an important question—whether visual explanations are "reliable or deceptive"—highlighting that explanation methods themselves can be flawed or misleading, and users should not assume all XAI tools are equally trustworthy without validation.
Abstract
Deep learning models have achieved remarkable success across diverse domains. However, the intricate nature of these models often impedes a clear understanding of their decision-making processes. This is where Explainable AI (XAI) becomes indispensable, offering intuitive explanations for model decisions. In this work, we propose a simple yet highly effective approach, ScoreCAM++, which introduces modifications to enhance the promising ScoreCAM method for visual explainability. Our proposed approach involves altering the normalization function within the activation layer utilized in ScoreCAM, resulting in significantly improved results compared to previous efforts. Additionally, we apply an activation function to the upsampled activation layers to enhance interpretability. This improvement is achieved by selectively gating lower-priority values within the activation layer. Through extensive experiments and qualitative comparisons, we demonstrate that ScoreCAM++ consistently achieves notably superior performance and fairness in interpreting the decision-making process compared to both ScoreCAM and previous methods.