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cs.AI2025
Towards the Formalization of a Trustworthy AI for Mining Interpretable Models explOiting Sophisticated Algorithms
Riccardo Guidotti, Martina Cinquini, Marta Marchiori Manerba +2
Interpretable-by-design models are crucial for fostering trust, accountability, and safe adoption of automated decision-making models in real-world applications. In this paper we f…
cs.AI2025
Interpretable Machine Learning for Oral Lesion Diagnosis through Prototypical Instances Identification
Alessio Cascione, Mattia Setzu, Federico A. Galatolo +2
Decision-making processes in healthcare can be highly complex and challenging. Machine Learning tools offer significant potential to assist in these processes. However, many curren…