@article{skespaceresearch24,
apice = {SkeSpaceresearch24},
author = {Sabbatini, Federico and Catia Grimani and Calegari, Roberta},
doi = {10.1016/j.asr.2024.04.041},
iris = {11585/995846},
journal = {Advances in Space Research},
keywords = {Space interferometers, LISA, Machine learning, Explainable clustering},
month = {July},
number = 1,
numpages = 13,
pages = {505--517},
title = {Bridging machine learning and diagnostics of the ESA LISA space mission with equation discovery via explainable artificial intelligence},
url = {https://www.sciencedirect.com/science/article/pii/S0273117724003880},
volume = 74,
year = 2024
}@article{ske-ia2024,
apice = {SkeIa2024},
author = {Sabbatini, Federico and Calegari, Roberta},
doi = {10.3233/IA-240026},
iris = {11585/995926},
journal = {Intelligenza Artificiale},
keywords = {Explainable clustering, explainable artificial intelligence, symbolic knowledge extraction, PSyKE},
number = 1,
numpages = 14,
pages = {21--34},
title = {Untying black boxes with clustering-based symbolic knowledge extraction},
url = {https://journals.sagepub.com/doi/10.3233/IA-240026},
urlpdf = {https://journals.sagepub.com/doi/pdf/10.3233/IA-240026},
volume = 18,
year = 2024
}@inproceedings{ske-aequitas24,
abstract = {This work explores the efficacy of symbolic knowledge-extraction (SKE) techniques in identifying biases and unfairness within opaque predictive models. Logic rules extracted from black-box predictors make it possible to verify if decisions are influenced by protected or sensitive features. In particular, the identifi- cation of biased or unfair decisions can be achieved through the evaluation of if-then rules, detecting the inclusion of protected and/or sensitive information in the rules' precondition. The effectiveness of SKE in this regard is demonstrated here by conducting various simulations on a well-known data set for loan grant prediction. Our findings highlight the potential of SKE as a valuable tool to reveal biases and discrimination in opaque predictions, ultimately contributing to the pursuit of fair and transparent decision-making systems.},
apice = {SkeAequitas24},
author = {Sabbatini, Federico and Calegari, Roberta},
booktitle = {Proceedings of the 2nd Workshop on Fairness and Bias in AI co-located with 27th European Conference on Artificial Intelligence (ECAI 2024)},
iris = {11585/1001068},
keywords = {Fairness in AI, Bias in AI, Explainable artificial intelligence, XAI, Symbolic knowledge extraction, PSyKE},
month = oct,
publisher = {Sun SITE Central Europe, RWTH Aachen University},
series = {CEUR Workshop Proceedings},
title = {Unmasking the Shadows: Leveraging Symbolic Knowledge Extraction to Discover Biases and Unfairness in Opaque Predictive Models},
url = {https://ceur-ws.org/Vol-3808/paper13.pdf},
venue = {Santiago de Compostela, Spain},
volume = 3808,
year = 2024
}@article{skemetrics-aaai2023,
address = {San Francisco, California},
apice = {SkemetricsAAAISpring2023},
author = {Sabbatini, Federico and Calegari, Roberta},
booktitle = {AAAI 2023 Spring Symposium Series},
doi = {10.1007/s43681-023-00406-1},
iris = {11585/995844},
journal = {AI and Ethics},
keywords = {Explainable artificial intelligence; Symbolic knowledge extraction; Readability metrics; AutoML},
month = mar,
publisher = {Springer Nature},
title = {On the Evaluation of the Symbolic Knowledge Extracted from Black Boxes},
year = 2023
}@article{skeislr-csur56,
acm = {3645103},
apice = {SkeislrCsur56},
articleno = 161,
author = {Ciatto, Giovanni and Sabbatini, Federico and Agiollo, Andrea and Magnini, Matteo and Omicini, Andrea},
dblp = {journals/csur/CiattoSAMO24},
doi = {10.1145/3645103},
eissn = {1557-734},
iris = {11585/969235},
issn = {0360-0300},
journal = {ACM Computing Surveys},
keywords = {Logic; Machine learning theory; Hybrid symbolic-numeric methods; Knowledge representation and reasoning},
lens = {143-064-043-213-611},
month = jun,
number = 6,
numpages = 35,
openalex = {W4391645809},
opencitations = {06804657002},
pages = {1--35},
publisher = {ACM},
scholar = {13701373869146776438},
scopus = {2-s2.0-85188835517},
semanticscholar = {267611660},
title = {Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review},
url = {https://dl.acm.org/doi/10.1145/3645103},
urlopenaccess = {https://dl.acm.org/doi/pdf/10.1145/3645103},
urlpdf = {https://dl.acm.org/doi/pdf/10.1145/3645103},
volume = 56,
wos = {WOS:001208566200027},
year = 2024
}@inproceedings{skemetrics-woa2024,
abstract = {In multi-agent systems, understanding the similarities and differences in agents' knowledge is essential for effective decision-making, coordination, and knowledge sharing. Current similarity metrics like cosine similarity, Jaccard similarity, and BERTScore are often too generic for comparing knowledge bases, overlooking critical aspects such as overlapping and fragmented boundaries, and varying domain densities. This paper introduces new specific similarity metrics for comparing knowledge bases, represented via symbolic knowledge. Our method compares local explanations of individual instances, preserving computational resources and providing a comprehensive evaluation of knowledge similarity. This approach addresses the limitations of existing metrics, enhancing the functionality and efficiency of multi-agent systems.},
apice = {SkemetricsWoa2024},
author = {Sabbatini, Federico and Christel Sirocchi and Calegari, Roberta},
booktitle = {WOA 2024 – 25th Workshop "From Objects to Agents 2024"},
editor = {Marco Alderighi and Matteo Baldoni and Cristina Baroglio and Roberto Micalizio and Stefano Tedeschi},
iris = {11585/995843},
keywords = {Multi-agent systems, Knowledge similarity, Symbolic knowledge},
title = {Symbolic Knowledge Comparison: Metrics and Methodologies for Multi-Agent Systems},
url = {https://ceur-ws.org/Vol-3735/paper_17.pdf},
urlpdf = {https://ceur-ws.org/Vol-3735/paper_17.pdf},
venue = {Bard, AO, Italy},
volume = 3735,
year = 2024
}
0000-0002-0532-6777