66 citations · 69 across the 4 of their papers we have counts for
4 papers · 1 filter
GLEAMS: Bridging the Gap Between Local and Global Explanations
Giorgio Visani, Vincenzo Stanzione, Damien Garreau
The explainability of machine learning algorithms is crucial, and numerous methods have emerged recently. Local, post-hoc methods assign an attribution score to each feature, indic…
Enabling Synthetic Data adoption in regulated domains
Giorgio Visani, Giacomo Graffi, Mattia Alfero +3
The switch from a Model-Centric to a Data-Centric mindset is putting emphasis on data and its quality rather than algorithms, bringing forward new challenges. In particular, the se…
Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes
Giorgio Visani, Federico Chesani, Enrico Bagli +2
In the global economy, credit companies play a central role in economic development, through their activity as money lenders. This important task comes with some drawbacks, mainly…
PSD2 Explainable AI Model for Credit Scoring
Neus Llop Torrent, Giorgio Visani, Enrico Bagli
The aim of this project is to develop and test advanced analytical methods to improve the prediction accuracy of Credit Risk Models, preserving at the same time the model interpret…