ReMoDEx: A Local-to-Global Relevance-Based Model Decision Explainability Framework for large-Scale Image Datasets
arXiv:2607.06889v1 Announce Type: cross Abstract: Deep learning image classifiers achieve strong predictive performance yet remain opaque in how decisions are formed. A model may predict correctly while relying on irrelevant cues, shortcut associations, peripheral structures, or device level artifacts instead of task relevant regions. On large scale datasets this opacity is especially problematic, since inspecting heatmaps one sample at a time cannot scale to thousands of predictions. We propose Relevance Based Model Decision Explainability (ReMoDEx), a framework for systematic, dataset scale
The increasing scale and complexity of deep learning models, especially on vast datasets, necessitate more robust and scalable explainability frameworks to ensure reliability and trust.
This framework addresses a critical barrier to deploying large-scale AI applications by providing a systematic way to understand and validate model decisions, mitigating risks associated with opaque 'black box' systems.
The ability to systematically explain AI model decisions over entire, large-scale image datasets, rather than individual samples, transforms how AI models can be evaluated, debugged, and trusted.
- · AI developers and researchers
- · Industries relying on computer vision (e.g., healthcare, autonomous vehicles)
- · Regulatory bodies
- · Companies relying on opaque AI systems for critical decisions
Improved debugging and robustness of large-scale AI image classification models.
Increased adoption of AI in high-stakes environments due to enhanced explainability and trust.
Potential for new regulatory frameworks for AI model explainability to emerge as a standard requirement.
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Read at arXiv cs.AI