arXiv:2607.06637v1 Announce Type: new Abstract: In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers. Central to our method is an optimization framework that modifies an instance so that the classifier predicts a specified target label, while ensuring that the modification remains easily explainable. The objective function contains two components: an explainability-aware $L_0$ (XA-$L_0$) penalty that promotes sparse and interpretable modifications, and a classifier loss objective that steers the perturbed instance to
The increasing complexity and opacity of AI models necessitate robust methods for explainability and assessing their limitations, particularly as they are deployed in critical applications.
This work introduces a unified framework to diagnose misclassification and gauge the robustness of black-box AI classifiers, directly addressing key concerns around AI trustworthiness and reliability.
The proposed optimization framework offers a concrete methodology for making AI model behavior more transparent and for quantifying their resilience to perturbed inputs.
- · AI developers
- · Auditors of AI systems
- · Sectors deploying critical AI (e.g., finance, healthcare)
- · AI explainability research
- · Developers of unexplainable 'black box' AI
- · Users vulnerable to adversarial attacks on AI
Improved understanding and debugging of complex AI models, leading to more reliable systems.
Increased adoption of AI in sensitive domains due to enhanced trust and regulatory compliance capabilities.
The development of a new class of 'explainability-aware' AI architectures and ethical AI auditing industries.
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