arXiv:2502.10311v3 Announce Type: replace Abstract: Most commonly used non-linear machine learning methods are closed-box models, uninterpretable to humans. The field of explainable artificial intelligence (XAI) aims to develop tools to examine the inner workings of these closed boxes. An often-used model-agnostic approach to XAI involves using simple models as local approximations to produce so-called local explanations; examples of this approach include LIME, SHAP, and SLISEMAP. This paper shows how a large set of local explanations can be reduced to a small "proxy set" of simple models, whi
The proliferation of complex, uninterpretable AI models necessitates advanced methods for understanding their decision-making processes, leading to continuous innovation in explainable AI.
Improving the interpretability of complex AI systems is critical for trust, debugging, and regulatory compliance, especially as AI integrates into sensitive applications.
This research provides a more efficient way to distil complex AI explanations into actionable insights, potentially broadening the adoption and reliability of XAI tools.
- · AI developers and researchers
- · Companies deploying AI in critical sectors
- · Regulatory bodies
- · Developers relying solely on black-box models
- · Traditional, less efficient XAI methods
More efficient and understandable AI interpretability will accelerate AI development and deployment.
Increased trust in AI systems could lead to their broader adoption in high-stakes environments, such as finance and healthcare.
Standardized and scalable explanation methods may enable new forms of AI auditing and compliance frameworks, impacting future AI governance.
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Read at arXiv cs.LG