Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features

Explainability is essential for high-stakes AI applications: The paper emphasizes that complex deep learning models cannot be reliably used in "risk-sensitive applications" without clear explanations of how they make decisions, highlighting why transparency matters for responsible AI deployment.

Aggregated visualizations reveal global patterns in AI decision-making: Rather than explaining individual predictions, the authors' method combines explanations across multiple samples to show which features consistently influence a model's classifications across a dataset, helping analysts understand systematic biases or patterns.

Feature importance varies across samples in meaningful ways: The visualization technique uses both color (to show classification impact) and size (to show variability) of features, allowing analysts to identify which features have consistent versus inconsistent effects—important for detecting when a model may be unreliable for certain data types.

Interactive tools enable deeper investigation of AI reasoning: The paper proposes drill-down visualizations and filtering capabilities that let analysts investigate specific features of interest in detail, supporting the kind of thorough auditing needed before deploying AI in sensitive domains.

Visual explanations can guide model improvement: By making global feature contributions transparent and analyzable, this approach enables practitioners to "derive adjustments to the AI model," suggesting that explainability tools can be practical instruments for actually improving AI systems rather than just documenting their flaws.