222 citations · 249 across the 7 of their papers we have counts for
7 papers
DTOR: Decision Tree Outlier Regressor to explain anomalies
Riccardo Crupi, Daniele Regoli, Alessandro Damiano Sabatino +5
Explaining outliers occurrence and mechanism of their occurrence can be extremely important in a variety of domains. Malfunctions, frauds, threats, in addition to being correctly i…
Evaluative Item-Contrastive Explanations in Rankings
Alessandro Castelnovo, Riccardo Crupi, Nicolò Mombelli +2
The remarkable success of Artificial Intelligence in advancing automated decision-making is evident both in academia and industry. Within the plethora of applications, ranking syst…
Disambiguation of Company names via Deep Recurrent Networks
Alessandro Basile, Riccardo Crupi, Michele Grasso +5
Name Entity Disambiguation is the Natural Language Processing task of identifying textual records corresponding to the same Named Entity, i.e. real-world entities represented as a…
Investigating Bias with a Synthetic Data Generator: Empirical Evidence and Philosophical Interpretation
Alessandro Castelnovo, Riccardo Crupi, Nicole Inverardi +2
Machine learning applications are becoming increasingly pervasive in our society. Since these decision-making systems rely on data-driven learning, risk is that they will systemati…
Counterfactual Explanations as Interventions in Latent Space
Riccardo Crupi, Alessandro Castelnovo, Daniele Regoli +1
Explainable Artificial Intelligence (XAI) is a set of techniques that allows the understanding of both technical and non-technical aspects of Artificial Intelligence (AI) systems.…
A Clarification of the Nuances in the Fairness Metrics Landscape
Alessandro Castelnovo, Riccardo Crupi, Greta Greco +3
In recent years, the problem of addressing fairness in Machine Learning (ML) and automatic decision-making has attracted a lot of attention in the scientific communities dealing wi…
BeFair: Addressing Fairness in the Banking Sector
Alessandro Castelnovo, Riccardo Crupi, Giulia Del Gamba +4
Algorithmic bias mitigation has been one of the most difficult conundrums for the data science community and Machine Learning (ML) experts. Over several years, there have appeared…