Per Year

 @article{psyke-ia16,
    author = {Sabbatini, Federico and Ciatto, Giovanni and Calegari, Roberta and Omicini, Andrea},
    dblp = {journals/ia/SabbatiniCCO22},
    doi = {10.3233/IA-210120},
    editor = {Calegari, Roberta and Ciatto, Giovanni and Omicini, Andrea and Vizzari, Giuseppe},
    eissn = {2211-0097},
    iris = {11585/890822},
    issn = {1724-8035},
    journal = {Intelligenza Artificiale},
    keywords = {Explainable AI, knowledge extraction, interpretable prediction, PSyKE},
    month = jul,
    number = 1,
    numpages = 22,
    pages = {27--48},
    publisher = {IOS Press},
    scholar = {7559675640918015038},
    scopus = {2-s2.0-85134193338},
    semanticscholar = {250400188},
    title = {Symbolic knowledge extraction from opaque {ML} predictors in {PSyKE}: Platform design \& experiments},
    url = {https://content.iospress.com/articles/intelligenza-artificiale/ia220141},
    volume = 16,
    wos = {WOS:000825367300003},
    year = 2022
} 

 @inproceedings{skemetrics-xaifin2022,
    address = {New York, NY, USA},
    author = {Sabbatini, Federico and Calegari, Roberta},
    booktitle = {Workshop on Explainable AI in Finance @ICAIF 2022},
    doi = {10.48550/arXiv.2211.00238},
    keywords = {Explainable artificial intelligence; Symbolic knowledge extraction; Readability metrics; AutoML},
    month = {November 2},
    scholar = {5021962022669458541},
    semanticscholar = {253244189},
    title = {Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper},
    url = {https://arxiv.org/abs/2211.00238},
    urlopenaccess = {https://arxiv.org/abs/2211.00238},
    year = 2022
} 

 @incollection{hypercube-woa2022,
    author = {Sabbatini, Federico and Ciatto, Giovanni and Calegari, Roberta and Omicini, Andrea},
    booktitle = {WOA 2022 -- 23rd Workshop ``From Objects to Agents''},
    dblp = {conf/woa/SabbatiniCCO22},
    editor = {Ferrando, Angelo and Mascardi, Viviana},
    iris = {11585/899358},
    issn = {1613-0073},
    keywords = {Explainable AI; Knowledge extraction; Interpretable prediction; PSyKE},
    month = nov,
    numpages = 13,
    pages = {48--60},
    publisher = {Sun SITE Central Europe, RWTH Aachen University},
    scholar = {8614662013642803891},
    scopus = {2-s2.0-85142519111},
    semanticscholar = {253270041},
    series = {CEUR Workshop Proceedings},
    subseries = {AIxIA Series},
    title = {Hypercube-Based Methods for Symbolic Knowledge Extraction: Towards a Unified Model},
    url = {http://ceur-ws.org/Vol-3261/paper4.pdf},
    urlopenaccess = {http://ceur-ws.org/Vol-3261/paper4.pdf},
    urlpdf = {http://ceur-ws.org/Vol-3261/paper4.pdf},
    volume = 3261,
    year = 2022
}