EXPECTATION Personalized Explainable AI for decentralized agents with heterogeneous knowledge

Towards a Unified Model for Symbolic Knowledge Extraction with Hypercube-Based Methods

2 min read Andrea Omicini Federico Sabbatini Giovanni Ciatto Roberta Calegari UNIBO 2023

by Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, and Andrea Omicini

Abstract

The XAI community is currently studying and developing symbolic knowledge-extraction (SKE) algorithms as a means to produce human-intelligible explanations for black-box machine learning predictors, so as to achieve believability in human-machine interaction. However, many extraction procedures exist in the literature, and choosing the most adequate one is increasingly cumbersome, as novel methods keep on emerging. Challenges arise from the fact that SKE algorithms are commonly defined based on theoretical assumptions that typically hinder practical applicability. This paper focuses on hypercube-based SKE methods, a quite general class of extraction techniques mostly devoted to regression-specific tasks. We first show that hypercube-based methods are flexible enough to support classification problems as well, then we propose a general model for them, and discuss how they support SKE on datasets, predictors, or learning tasks of any sort. Empirical examples are reported as well –based upon the PSyKE framework –, showing the applicability of hypercube-based methods to actual classification tasks.

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Bibtex 

@article{hypercubeske-ia17,
    author = {Sabbatini, Federico and Ciatto, Giovanni and Calegari, Roberta and Omicini, Andrea},
    dblp = {journals/ia/SabbatiniCCO23},
    doi = {10.3233/IA-230001},
    editor = {Ferrando, Angelo and Mascardi, Viviana},
    iris = {11585/941033},
    journal = {Intelligenza Artificiale},
    keywords = {explainable AI, knowledge extraction, interpretable prediction, PSyKE},
    month = jun,
    note = {Special issue: Selected papers from the 23rd Workshop ``From Objects to Agents'' (WOA 2022)},
    number = 1,
    numpages = 13,
    pages = {63--75},
    publisher = {IOS Press},
    scholar = {14669296704428238758},
    scopus = {2-s2.0-85168332389},
    semanticscholar = {259324728},
    title = {Towards a Unified Model for Symbolic Knowledge Extraction with Hypercube-Based Methods},
    url = {https://content.iospress.com/articles/intelligenza-artificiale/ia230001},
    urlopenaccess = {https://cris.unibo.it/retrieve/3a2a510e-9dc5-4b07-b3f7-398cf2f21419/ia-2023-psyke.pdf},
    volume = 17,
    year = 2023
}