Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy

Making AI explanations accessible requires the right tools: The paper introduces three software packages (Zennit, CoRelAy, and ViRelAy) designed to make it easier for researchers to understand how deep neural networks make decisions, addressing the fact that "manual approaches exist, but without the right tools they are unnecessarily labor intensive."

Understanding individual predictions isn't enough for responsible AI: While existing methods can explain single predictions, the software enables "dataset-wide analyses" of explanations, suggesting that responsible AI requires examining patterns across many examples rather than just spot-checking individual cases.

Standardization improves trustworthiness: The paper emphasizes providing "a standardized implementation solution for XAI, to contribute towards more reproducibility in our field," highlighting that reproducible, consistent tools are essential for building confidence in AI explanations across different organizations and research teams.

Interactive exploration helps catch hidden biases: ViRelAy's web-based interface for "interactively explore[ing] data, attributions, and analysis results" suggests that visual, hands-on tools can help researchers discover problematic patterns in how AI models make decisions that might be missed in purely statistical analyses.