5 papers
Beyond Explaining Predictions: Logic-Based Explanations for Confidence in Machine Learning Models
VinÃcius Peixoto Chagas, Carlos Henrique Leitão Cavalcante, Thiago Alves Rocha
Machine learning is increasingly used in critical domains, where both predictions and their associated confidence levels influence important decisions. To enhance transparency in s…
Concisely Explaining the Doubt: Minimum-Size Abductive Explanations for Linear Models with a Reject Option
Gleilson Pedro Fernandes, Thiago Alves Rocha
Trustworthiness in artificial intelligence depends not only on what a model decides, but also on how it handles and explains cases in which a reliable decision cannot be made. In c…
Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy
VinÃcius P. Chagas, Luiz H. T. Viana, Mac M. da S. Carlos +4
Sudden cardiac death (SCD) is unpredictable, and its prediction in Chagas cardiomyopathy (CC) remains a significant challenge, especially in patients not classified as high risk. W…
Enhancing Framingham Cardiovascular Risk Score Transparency through Logic-Based XAI
Emannuel L. de A. Bezerra, Luiz H. T. Viana, VinÃcius P. Chagas +3
Cardiovascular disease (CVD) remains one of the leading global health challenges, accounting for more than 19 million deaths worldwide. To address this, several tools that aim to p…
Slice and Explain: Logic-Based Explanations for Neural Networks through Domain Slicing
Luiz Fernando Paulino Queiroz, Carlos Henrique Leitão Cavalcante, Thiago Alves Rocha
Neural networks (NNs) are pervasive across various domains but often lack interpretability. To address the growing need for explanations, logic-based approaches have been proposed…