Per Year

@incollection{fairbridge-hiccs2025,
    apice = {FairnessHicss2025},
    author = {Ciatto, Giovanni and Matteini, Mattia and Laura Sartori and Maria Rebrean and Catelijne Muller and Andrea Borghesi and Calegari, Roberta},
    booktitle = {Proceedings of the 58th Hawaii International Conference on System Sciences},
    doi = {10.24251/HICSS.2025.777},
    iris = {11585/1018911},
    isbn = {978-0-9981331-8-8},
    keywords = {AI and Digital Discrimination, artificial intelligence, bias, design, fairness, multi-disciplinarity},
    location = {Hawaii, HI, USA},
    numpages = 10,
    openalex = {w4407208122},
    pages = {6504--6513},
    scholar = {5084539886289942855},
    scopus = {2-s2.0-105005142003},
    title = {{AI}-fairness: the {FAIRBRIDGE} approach to practically bridge the gap between socio-legal and technical perspectives},
    url = {https://hdl.handle.net/10125/109625},
    wos = {WOS:001443246900761},
    year = 2025
}

@inproceedings{aequitas24bellatreccia,
    abstract = {AI-based diagnosis of skin diseases holds considerable promise for increasing healthcare accessibility, however, its effectiveness is currently limited by several challenges, including fairness. This study analyzes a real-world dataset collected from an Italian hospital, characterized by limited data availability, leading to poor diversity and representation—particularly evident in the scarcity of data for certain diseases and darker skin tones. Such limitations result in substantial classification biases. Additionally, the dataset includes non-dermoscopic, consumer-grade images that suffer from quality issues like inconsistent lighting and blurriness, complicating the training of fair and efficient AI models. Conventional strategies to mitigate these problems, such as synthesizing images for underrepresented groups, are hindered by the difficulty in accurately identifying skin tones from poor-quality images. Our research introduces a novel pipeline designed to enhance both the accuracy and fairness of skin disease diagnosis by addressing the challenges posed by real-world data. The proposed solution involves a two-stage approach: 1) data pre-processing and augmentation to obtain images that more accurately represent darker skin tones, generated through a state-of-the-art diffusion model; and 2) disease classification employing deep learning models. This methodology addresses data scarcity and improves fairness, with thorough validation of real-world data showing enhanced reliability and fairness in predictions across various skin diseases.},
    apice = {Aequitas24Bellatreccia},
    author = {Chiara Bellatreccia and Daniele Zama and Arianna Dondi and Luca Pierantoni and Andreozzi Laura and Iria Neri and Marcello Lanari and Andrea Borghesi and Calegari, Roberta},
    booktitle = {Proceedings of the 2nd Workshop on AI bias: Measurements, Mitigation, Explanation Strategies},
    iris = {11585/1018912},
    publisher = {CEUR Workshop Proceedings},
    title = {Addressing Bias and Data Scarcity in AI-Based Skin Disease Diagnosis with Non-Dermoscopic Images},
    url = {https://ceur-ws.org/Vol-3961/paper8.pdf},
    venue = {Barcelona, Spain},
    volume = 3961,
    year = 2025
}

@inproceedings{ske-aixia2024,
    abstract = {Adopting opaque machine learning predictors, which achieve very high predictive performance, often necessitates incorporating symbolic knowledge-extraction techniques. These techniques aim to explain the opaque predictions, thus making them applicable in high-stakes scenarios. The development of symbolic knowledge-extraction procedures is evolving alongside the dynamic machine learning landscape. However, there are recurring drawbacks that tend to be overlooked or addressed in a suboptimum way. Common examples include the non-exhaustiveness of the global explanations generated for a black-box predictor or the unwanted discretisation introduced in the prediction of continuous variables. To tackle these challenges, in this work, we introduce the HEx algorithm, its formalisation and its properties. This algorithm aims to obtain a symbolic, hierarchical representation of the knowledge acquired by opaque machine learning classifiers and regressors, always ensuring knowledge exhaustiveness and avoiding any output discretisation. Experiments demonstrating the superior capabilities of HEx compared to state-of-the-art competitors in terms of predictive performance, completeness, and human readability are presented.},
    apice = {SkeAixia2024},
    author = {Sabbatini, Federico and Calegari, Roberta},
    booktitle = {Advances in Artificial Intelligence},
    doi = {10.1007/978-3-031-80607-0_20},
    iris = {11585/1018918},
    keywords = {Explainable artificial intelligence, Symbolic knowledge extraction, PSyKE},
    publisher = {Springer, Cham},
    series = {Lecture Notes in Computer Science},
    subseries = {AIxIA 2024},
    title = {Hierarchical Knowledge Extraction from Opaque Machine Learning Predictors},
    url = {https://link.springer.com/10.1007/978-3-031-80607-0_20},
    volume = 15450,
    year = 2025
}

@incollection{skemetrics-aixia2024,
    apice = {SkemetricsAixia2024},
    author = {Sabbatini, Federico and Calegari, Roberta},
    doi = {10.1007/978-3-031-80607-0_19},
    iris = {11585/1018915},
    keywords = {Explainable artificial intelligence, Symbolic knowledge extraction, AutoML},
    numpages = 16,
    pages = {241--256},
    title = {ICE: An Evaluation Metric to Assess Symbolic Knowledge Quality},
    url = {https://link.springer.com/10.1007/978-3-031-80607-0_19},
    year = 2025
}