Evaluating Explainability in Safety-Critical ATR Systems: Limitations of Post-Hoc Methods and Paths Toward Robust XAI

📰 ArXiv cs.AI

Learn to evaluate explainability in safety-critical ATR systems and overcome limitations of post-hoc methods for robust XAI

advanced Published 9 May 2026

Action Steps

Evaluate the limitations of post-hoc explainability methods in ATR systems
Assess the need for robust XAI in safety-critical environments
Develop and implement model-agnostic explainability techniques
Validate and test explainability methods for reliability and interpretability
Integrate XAI into the ATR system development pipeline

Who Needs to Know This

Machine learning engineers and researchers working on safety-critical ATR systems can benefit from this knowledge to ensure reliable and interpretable model decisions

Key Insight

💡 Post-hoc explainability methods have limitations in safety-critical ATR systems, and robust XAI is essential for reliable and interpretable model decisions

Key Takeaways

Learn to evaluate explainability in safety-critical ATR systems and overcome limitations of post-hoc methods for robust XAI

Full Article

Title: Evaluating Explainability in Safety-Critical ATR Systems: Limitations of Post-Hoc Methods and Paths Toward Robust XAI

Abstract:
arXiv:2605.05748v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) is increasingly rec ognized as essential for deploying machine learning systems in safety critical environments. In Automatic Target Recognition (ATR), where models operate on image, video, radar, and multisensor data, high pre dictive performance alone is insufficient. Model decisions must also be interpretable, reliable, and suitable for validation. This paper presents a structured evaluation of explainab

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