arXiv:2605.21646v1 Announce Type: new Abstract: Prototype-based explanations offer an intuitive, example-based approach to support the interpretability of machine learning black box classifiers but often lack feature-level granularity. We introduce a framework that integrates feature importance at two levels to address this gap. First, for local explanations, we propose \textit{alike parts}: a method that uses feature importance scores to highlight the most relevant, shared feature subsets between a classified instance and its nearest prototype, guiding user attention. Second, we augment the g
This research addresses the increasing demand for transparent and interpretable AI models, crucial for trust and adoption in complex applications.
Improved interpretability in black-box AI models enhances debugging, promotes fair algorithm development, and satisfies regulatory requirements for transparency.
Machine learning explanations become more granular and actionable, moving beyond simple examples to detailed feature-level insights.
- · AI developers
- · Regulatory bodies
- · Industries adopting AI
- · Opaque AI solutions
- · Companies with poor AI explainability practices
Increased user trust and adoption of AI systems due to better understanding of their decisions.
Faster development and deployment of robust and ethical AI applications across various sectors.
New industry standards and tools emerge for 'explainable AI as a service,' integrated into core ML pipelines.
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