191 citations · 332 across the 13 of their papers we have counts for
14 papers
Ensemble of Counterfactual Explainers
Riccardo Guidotti, Salvatore Ruggieri
In eXplainable Artificial Intelligence (XAI), several counterfactual explainers have been proposed, each focusing on some desirable properties of counterfactual instances: minimali…
Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference
Jose M. Alvarez, Salvatore Ruggieri
We present counterfactual situation testing (CST), a causal data mining framework for detecting discrimination in classifiers. CST aims to answer in an actionable and meaningful wa…
AUC-based Selective Classification
Andrea Pugnana, Salvatore Ruggieri
Selective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This fra…
Fairness Implications of Encoding Protected Categorical Attributes
Carlos Mougan, Jose M. Alvarez, Salvatore Ruggieri +1
Past research has demonstrated that the explicit use of protected attributes in machine learning can improve both performance and fairness. Many machine learning algorithms, howeve…
Estimating the Total Volume of Queries to a Search Engine
Fabrizio Lillo, Salvatore Ruggieri
We study the problem of estimating the total number of searches (volume) of queries in a specific domain, which were submitted to a search engine in a given time period. Our statis…
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Mirco Nanni, Gennady Andrienko, Albert-László Barabási +36
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when…
Bias in Data-driven AI Systems -- An Introductory Survey
Eirini Ntoutsi, Pavlos Fafalios, Ujwal Gadiraju +20
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and any…
On The Stability of Interpretable Models
Riccardo Guidotti, Salvatore Ruggieri
Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in is…
Open the Black Box Data-Driven Explanation of Black Box Decision Systems
Dino Pedreschi, Fosca Giannotti, Riccardo Guidotti +4
Black box systems for automated decision making, often based on machine learning over (big) data, map a user's features into a class or a score without exposing the reasons why. Th…
Local Rule-Based Explanations of Black Box Decision Systems
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri +3
The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations f…
SCube: A Tool for Segregation Discovery
Alessandro Baroni, Salvatore Ruggieri
Segregation is the separation of social groups in the physical or in the online world. Segregation discovery consists of finding contexts of segregation. In the modern digital soci…
Efficiently Clustering Very Large Attributed Graphs
Alessandro Baroni, Alessio Conte, Maurizio Patrignani +1
Attributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into c…
Causal Inference for Social Discrimination Reasoning
Bilal Qureshi, Faisal Kamiran, Asim Karim +2
The discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning a…
Porting Decision Tree Algorithms to Multicore using FastFlow
Marco aldinucci, Salvatore Ruggieri, Massimo Torquati
The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine…