Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures
arXiv:2606.00557v1 Announce Type: new Abstract: To understand how a neural network (NN) functions and makes predictions, it has become increasingly clear that analyzing only the input domain is insufficient -- one must also examine its internal inference mechanisms to capture the complete picture. To explain the internal inference mechanisms of such models, it is essential to analyze the importance of latent representations for a given task. In this paper, we propose the \emph{normalized relevance measure} (NRM) framework -- a novel general explanation procedure that attributes relevance to \e
The increasing complexity and opacity of neural networks necessitate robust explainability frameworks to foster trust and facilitate responsible AI deployment.
Understanding the internal mechanisms of AI models is crucial for debugging, ensuring fairness, and advancing AI research, particularly as AI systems integrate into critical infrastructure.
This research provides a new tool for interpreting neural network decisions, potentially leading to more transparent and auditable AI systems.
- · AI developers
- · AI researchers
- · Regulatory bodies
- · Industries relying on AI explainability
- · AI models lacking interpretability by design
- · Black-box AI proponents
Improved interpretability of neural networks will enable more confident deployment of AI in sensitive applications.
Enhanced explainability tools will accelerate the development of more reliable and ethical AI systems, reducing bias and improving trust.
Standardisation of explainability metrics could lead to new regulatory frameworks for AI transparency and accountability across various sectors.
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