Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations
arXiv:2606.28391v1 Announce Type: cross Abstract: The wide use of Convolutional Neural Networks (CNN) in numerous domains and real-world classification applications is justified by their high precision and automation speed, helping users concentrate on higher-expertise tasks. To better understand the models and avoid bias during deployment, eXplainable Artificial Intelligence (XAI) techniques can be used after training. But as the list of XAI solutions expand, comparisons between them diverge, and consensus over their evaluation cannot be reached. This paper proposes a variation of Fidelity-ba
The proliferation of AI models in critical applications demands better understanding and evaluation of their decision processes to ensure responsible deployment.
Improved methods for evaluating AI explanations are crucial for building trust, preventing bias, and validating CNN classifiers in real-world, high-stakes scenarios.
This research provides a new methodology for evaluating XAI techniques, potentially leading to more reliable and comparable assessments of AI model explainability.
- · AI ethicists
- · Developers of XAI tools
- · Industries deploying CNNs in sensitive applications
- · Unreliable XAI methods
- · Systems with poorly understood AI biases
More rigorous evaluation benchmarks for eXplainable AI techniques become standard.
Increased adoption of well-vetted XAI solutions in regulatory and auditing frameworks for AI.
Enhanced public trust in AI systems due to greater transparency and understanding of their decision-making.
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Read at arXiv cs.AI