arXiv:2505.15516v3 Announce Type: replace-cross Abstract: While eXplainable AI (XAI) has advanced significantly, few methods address interpretability in embedded vector spaces where dimensions represent complex abstractions. We introduce Distance Explainer, a novel method for generating local, post-hoc explanations of embedded spaces in machine learning models. Our approach adapts saliency-based techniques from RISE to explain the distance between two embedded data points by assigning attribution values through selective masking and distance-ranked mask filtering. We evaluate Distance Explaine
The increasing complexity and opacity of AI models necessitate better interpretability, making explainability a critical research area for adoption and regulation.
Improved explainable AI (XAI) for embedding spaces is crucial for debugging, auditing, and building trust in advanced AI systems, particularly those used in sensitive applications.
This research introduces a novel method to explain the relationships within complex embedded vector spaces, offering a new tool for understanding the internal workings of AI models.
- · AI developers
- · AI auditors
- · Regulatory bodies
- · Sectors using complex AI (e.g., finance, healthcare)
- · Opaque black-box AI systems
- · Developers neglecting interpretability
Increased ability to diagnose and improve AI model performance and fairness by understanding latent representations.
Faster development and deployment of robust AI systems due to enhanced transparency, potentially accelerating AI adoption in regulated industries.
New standards and regulations around AI explainability could emerge, making XAI tools like Distance Explainer indispensable for compliance.
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Read at arXiv cs.AI