Roberta Calegari

Co-design of a trustworthy AI-based prognostic tool for predicting patient outcome in acute stroke

This paper reports a descriptive, process-oriented case study of the application of Z-Inspection®, an ethically aligned co-design methodology, in the early design phase of an artificial intelligence (AI) system for healthcare. The methodology was applied within the Horizon Europe project VALIDATE [1], which develops an...

Fairness-Aware Knowledge Distillation via Symbolic Knowledge Extractors

Individual and group fairness assessments via counterfactual explanations

This study explores the potential of counterfactual explanations to assess artificial intelligence (AI) fairness, especially in critical decision-making systems. Predictive models may amplify biases inherent in data sets or algorithms, and given the absence of a universally accepted fairness metric, a case-specific app...

ActMap: Single-Pass Uncertainty Quantification from Generation-Time Activation Maps

ActMap is introduced, a white-box representation that compresses the generation-time hidden-state trajectory into a fixed tensor of temporal-statistic channels that preserves structure across transformer depth and pooled hidden coordinates, and supports abstention, routing, and selective verification from a single gene...