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Interpretable individuaL Latent neUral Mapping for post-hoc Explanations of high-dimensional tabular data applied to science

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Interpretable individuaL Latent neUral Mapping for post-hoc Explanations of high-dimensional tabular data applied to science Explainable Artificial Intelligence (XAI) is essential for deploying trustworthy AI systems, especially in critical fields where transparency is key. Our XAI ERC project (2019-2025) has focused on developing post-hoc “explainers” for black-box machine learning models. A key outcome of this project is ILLUME, a meta-explainer that generates a complex global surrogate model to mimic a black-box system. When applied to individual inputs, this model becomes interpretable and can produce multiple explanation formats, including feature relevance, decision rules, and counterfactuals, for tabular data. Compared to competitors, ILLUME has shown superior accuracy, robustness, and quality, establishing itself as a breakthrough post-hoc XAI approach. Our current proposal aims to consolidate ILLUME's capabilities in two main intertwined directions: i) Demonstrate Scientific Impact: We will prove ILLUME's effecti

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recordType

award

status

SIGNED

region

EU

value

150000

unit

EUR