Per anno

@proceedings{proceedings-aequitas2024,
    apice = {ProceedingsAequitasEcai2024},
    editor = {Calegari, Roberta and Virginia Dignum and Barry O'Sullivan},
    iris = {11585/996783},
    publisher = {CEUR Workshop Proceedings},
    series = {AI*IA Series},
    title = {Proceedings of the 2nd Workshop on Fairness and Bias in AI co-located with 27th European Conference on Artificial Intelligence (ECAI 2024)},
    url = {https://ceur-ws.org/Vol-3808/},
    volume = 3808,
    year = 2024
}

@article{sijair-fairness23,
    apice = {SIJairFairness23},
    editor = {Calegari, Roberta and Andrea Aler Tubella and Virginia Dignum and Milano, Michela},
    journal = {Journal of Artificial Intelligence Research},
    keywords = {AI Fairness, AI Bias},
    title = {Fairness and Bias in AI},
    url = {https://www.jair.org/index.php/jair/SpecialTrack-FBAI},
    year = 2024
}

@article{children24,
    abstract = {Background: Pediatric dermatology represents one of the most underserved subspecialties in pediatrics. Artificial intelligence (AI) and telemedicine have become considerable in dermatology, reaching diagnostic accuracy comparable to or exceeding that of in-person visits. This work aims to review the current state of telemedicine and AI in pediatric dermatology, suggesting potential ways to address existing issues and challenges. Methods: We conducted a literature review including only articles published in the last 15 years. A total of 458 studies were identified, of which only 76 were included. Results: Most of the studies on telemedicine evaluate accuracy focused on concordance, which ranges from 70% to 89% for the most common pediatric skin diseases. Telemedicine showed the potential to manage chronic dermatological conditions in children, as well as decrease waiting times, and represents the chance for unprivileged populations to overcome barriers limiting access to medical care. The main limitations of telemedicine consist of the language barrier and the need for adequate technologies and acceptable image-quality video, which can be overcome by AI. AI-driven apps and platforms can facilitate remote consultations between pediatric dermatologists and patients or their caregivers. However, the integration of AI into clinical practice faces some challenges ranging from technical to ethical and regulatory. It is crucial to ensure that the development, deployment, and utilization of AI systems conform to the seven fundamental requirements for trustworthy AI. Conclusion: This study supplies a detailed discussion of open challenges with a particular focus on equity and ethical considerations and defining possible concrete directions.},
    apice = {Children24},
    author = {Daniele Zama and Andrea Borghesi and Alice Ranieri and Elisa Manieri and Luca Pierantoni and Laura Andreozzi and Arianna Dondi and Iria Neri and Marcello Lanari and Calegari, Roberta},
    doi = {10.3390/children11111401},
    iris = {11585/1000949},
    journal = {Children},
    publisher = {MDPI},
    title = {Perspectives and Challenges of Telemedicine and Artificial Intelligence in Pediatric Dermatology},
    url = {https://doi.org/10.3390/children11111401},
    volume = 11,
    year = 2024
}

@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
}

@incollection{skinimagesaaai2024,
    apice = {SkinimagesAaai2024},
    author = {Andrea Borghesi and Calegari, Roberta},
    booktitle = {AI for Health Equity and Fairness},
    doi = {10.1007/978-3-031-63592-2_5},
    iris = {11585/979615},
    month = {Aug},
    numpages = 18,
    pages = {47--64},
    publisher = {Springer, Cham},
    series = {Studies in Computational Intelligence},
    title = {Generation of Clinical Skin Images with Pathology with Scarce Data},
    url = {https://link.springer.com/10.1007/978-3-031-63592-2_5},
    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{fairanki-jcai2024,
    apice = {FairankIjcai2024},
    author = {Eleonora Misino and Calegari, Roberta and Michele Lombardi and Michela Milano},
    booktitle = {Proceedings of the 33rd International Joint Conference on Artificial Intelligence AI for Good},
    doi = {10.24963/ijcai.2024/820},
    iris = {11585/984254},
    keywords = {AI Ethics, Trust, Fairness},
    numpages = 9,
    pages = {7412--7420},
    title = {Ensuring Fairness Stability for Disentangling Social Inequality in Access to Education: the FAiRDAS General Method},
    url = {https://doi.org/10.24963/ijcai.2024/820},
    urlopenaccess = {https://www.ijcai.org/proceedings/2024/0820.pdf},
    urlpdf = {https://www.ijcai.org/proceedings/2024/0820.pdf},
    venue = {IJCAI 2024},
    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
}

@inproceedings{fauci-aequitas2024,
    apice = {FauciAequitas2024},
    articleno = 8,
    author = {Magnini, Matteo and Ciatto, Giovanni and Calegari, Roberta and Omicini, Andrea},
    booktitle = {AEQUITAS 2024: Fairness and Bias in AI},
    dblp = {conf/aequitas/MagniniCCO24},
    editor = {Calegari, Roberta and Dignum, Virginia and O'Sullivan, Barry},
    iris = {11585/995740},
    keywords = {AI Fairness, FaUCI, in-processing, regularization, mitigation},
    month = oct,
    note = {Proceedings of the 2nd Workshop on Fairness and Bias in AI co-located with 27th European Conference on Artificial Intelligence (ECAI 2024)},
    numpages = 13,
    pages = {1--13},
    publisher = {CEUR-WS},
    scholar = {12743200180573703475},
    scopus = {2-s2.0-85210042328},
    series = {CEUR Workshop Proceedings},
    title = {Enforcing Fairness via Constraint Injection with {FaUCI}},
    url = {https://ceur-ws.org/Vol-3808/paper8.pdf},
    urlopenaccess = {https://ceur-ws.org/Vol-3808/paper8.pdf},
    urlpdf = {https://ceur-ws.org/Vol-3808/paper8.pdf},
    volume = 3808,
    wos = {WOS:001752291200008},
    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{siacmtai23,
    apice = {SiAcmTai23},
    doi = {10.1145/3649452},
    editor = {Calegari, Roberta and Fosca Giannotti and Francesca Pratesi and Milano, Michela},
    iris = {11585/996787},
    journal = {ACM Computing Surveys},
    month = apr,
    number = 7,
    publisher = {ACM},
    title = {Special Issue on Trustworthy AI},
    url = {https://dl.acm.org/doi/10.1145/3649452},
    urlpdf = {https://dl.acm.org/pb-assets/static_journal_pages/csur/pdf/CSUR-CFP-Trustworthy-AI_083022-1661888117770.pdf},
    volume = 56,
    year = 2024
}

@inproceedings{longtermfairness-aequitas24,
    abstract = {Recent advancements have made significant progress in addressing fair ranking and fairness with continuous sensitive attributes as separate challenges. However, their intersection remains underexplored, although crucial for guaranteeing a wider applicability of fairness requirements. In many real-world contexts, sensitive attributes such as age, weight, income, or degree of disability are measured on a continuous scale rather than in discrete categories. Addressing the continuous nature of these attributes is essential for ensuring effective fairness in such scenarios. This work aims to fill the gap in the existing literature by proposing a novel methodology that integrates state-of-the-art techniques to address long- term fairness in the presence of continuous protected attributes. We demonstrate the effectiveness and flexibility of our approach using real-world data.},
    apice = {LongtermfairnessAequitas24},
    author = {Luca Giuliani and Eleonora Misino and Calegari, Roberta and Michele Lombardi},
    booktitle = {Proceedings of the 2nd Workshop on Fairness and Bias in AI co-located with 27th European Conference on Artificial Intelligence (ECAI 2024)},
    iris = {11585/998075},
    month = oct,
    title = {Long-Term Fairness Strategies in Ranking with Continuous Sensitive Attributes},
    url = {https://ceur-ws.org/Vol-3808/paper11.pdf},
    venue = {Santiago de Compostela, Spain},
    volume = 3808,
    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
}

@inproceedings{equalopportunity-aequitas24,
    abstract = {This study focuses on predicting students' academic performance, examining how AI predictive models often reflect socioeconomic inequalities influenced by factors such as parental socioeconomic status and home environ- ment, which affect the fairness of predictions. We compare three AI models aimed at performing an ablation study to understand how these sensitive features (referred to as circumstances) influence predictions. Our findings reveal biases in predictions that favor advantaged groups, depending on whether the goal is to identify excellence or underperformance. Additionally, a two-stage estimation procedure is proposed in the third model to mitigate the impact of sensitive features on predictions, thereby offering a model that can be considered fair with respect to inequality of opportunity.},
    apice = {EqualopportunityAequitas24},
    author = {Ángel S. Marrero and Gustavo A. Marrero and Carlos Bethencourt and Liam James 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 = {11585/1001050},
    keywords = {AI-fairness, socioeconomic equality of opportunity, AI-ethics},
    month = {Oct},
    series = {CEUR Workshop Proceedings},
    title = {AI-fairness and equality of opportunity: a case study on educational achievement},
    url = {https://ceur-ws.org/Vol-3808/paper17.pdf},
    volume = 3808,
    year = 2024
}