Alessio Arcudi

Ritratto di Alessandro Arcudi

Computer Science and Innovation for Societal Challenges, XXXVI series
Gian Antonio Susto

Project description
ML and Anomaly Detection solutions provide automatic and intelligent tools that lead to waste prevention and optimized use of resources, but a lack of understanding of these black-box tools can have a negative effect on various aspects, like trust and adoption of the model, improvement and robustness in critical conditions. Research interests: Explanations of models. Strengthening interpretability by introducing explicit constraints on hidden representations on architecture can shed light on the reliability of black-box models, as well as directly include humans in the optimization cycle, leveraging their feedback to improve interpretability of the models. The explanations not only have a good effect on the reliability of the ML models, but also provide information on the causes that lead to an anomaly in the process.