To believe or not to believe: Validating explanation fidelity for dynamic malware analysis

Explanation methods for AI systems require validation against expert knowledge: The researchers found that visual explanations for malware detection were only reliable when validated against actual security domain expertise. In their second case study, the same explanation method produced "ambiguous interpretation" depending on how the data was formatted, highlighting that high AI accuracy doesn't guarantee trustworthy explanations.

How you represent data to AI systems fundamentally affects whether explanations are meaningful: When malware API data was visualized as sequences and frequencies versus simple existence, the explanation method produced different results—some insightful (identifying cryptography APIs) and some unclear. This suggests that responsible AI deployment requires careful consideration of data representation choices.

Visual explanations for complex technical systems need domain expert oversight: The researchers successfully identified that the AI was focusing on "indirect calls that uniquely characterize exploit behavior," but only because they had security experts available to verify these findings. This underscores that AI explanations in specialized fields like cybersecurity cannot be trusted without expert validation.

Current explanation techniques have significant limitations that aren't always obvious: Despite achieving "high classification effectiveness" in their deep learning models, the explanation methods sometimes failed to provide clear insights. This demonstrates that a well-performing AI system can still produce unreliable or ambiguous explanations—a critical concern for responsible deployment.