Part-based Quantitative Analysis for Heatmaps

Heatmaps are widely used to explain AI decisions, but their interpretation remains largely subjective and requires expert knowledge, limiting their practical use for making AI systems more transparent to general users and stakeholders.

The paper addresses a critical gap in responsible AI: the lack of automatic, numerical methods to evaluate heatmap quality objectively. Current approaches depend on human judgment, which is inconsistent, expensive, and difficult to scale across many AI systems.

Developing granular evaluation metrics for heatmaps is essential for responsible AI deployment, as it enables organizations to systematically verify whether their explainability tools are actually highlighting the right features that drive AI decisions—rather than relying on subjective visual inspection.

Making heatmap analysis more cost-effective and accessible to non-experts could democratize AI transparency, allowing broader teams (not just domain specialists) to audit and understand how AI systems make decisions in high-stakes applications like healthcare or criminal justice.

Quantitative analysis of heatmaps supports accountability in AI systems by providing measurable evidence of what features an AI model relies on, which is crucial for identifying potential biases, errors, or unfair decision-making patterns.