Achieving Complete Coverage with Hypercube-Based Symbolic Knowledge-Extraction Techniques

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Symbolic knowledge-extraction (SKE) techniques are currently employed for various purposes, particularly addressing the challenge of explaining opaque models by generating human-understandable explanations. The existing literature encompasses a diverse range of techniques, each relying on specific theoretical assumptions and possessing their own advantages and disadvantages. Amongst the available choices, hypercube-based SKE techniques are notable for their adaptability and versatility. However, they may suffer from limited completeness when utilised for making predictions. This research aims to augment the predictive capabilities of hypercube-based SKE techniques, striving for a completeness rate of 100\%. Furthermore, the study includes experiments that assess the effectiveness of the proposed enhancements.

keywordsSymbolic knowledge extraction, Explainable artificial intelligence, PSyKE
funding project
wrenchAEQUITAS — Assessment and Engineering of eQuitable, Unbiased, Impartial and Trustworthy Ai Systems (01/11/2022–31/10/2025)

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