297 citations · 1.5k across the 35 of their papers we have counts for
36 papers
The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems
Gabriele Barlacchi, Margherita Lalli, Emanuele Ferragina +3
Recommender systems shape individual choices through feedback loops in which user behavior and algorithmic recommendations coevolve over time. The systemic effects of these loops r…
A Simulation Framework for Studying Systemic Effects of Feedback Loops in Recommender Systems
Gabriele Barlacchi, Margherita Lalli, Emanuele Ferragina +2
Recommender systems continuously interact with users, creating feedback loops that shape both individual behavior and collective market dynamics. This paper introduces a simulation…
Interpretable and Fair Mechanisms for Abstaining Classifiers
Daphne Lenders, Andrea Pugnana, Roberto Pellungrini +3
Abstaining classifiers have the option to refrain from providing a prediction for instances that are difficult to classify. The abstention mechanism is designed to trade off the cl…
Explanations Go Linear: Post-hoc Explainability for Tabular Data with Interpretable Meta-Encoding
Simone Piaggesi, Riccardo Guidotti, Fosca Giannotti +1
Post-hoc explainability is essential for understanding black-box machine learning models. Surrogate-based techniques are widely used for local and global model-agnostic explanation…
A survey on the impacts of recommender systems on users, items, and human-AI ecosystems
Luca Pappalardo, Salvatore Citraro, Giuliano Cornacchia +12
Recommendation systems and assistants (in short, recommenders) influence through online platforms most actions of our daily lives, suggesting items or providing solutions based on…
AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
Clara Punzi, Roberto Pellungrini, Mattia Setzu +2
Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions. This growing presence means that humans are now constantly interact…
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis
Nafis Irtiza Tripto, Adaku Uchendu, Thai Le +3
Authorship Analysis, also known as stylometry, has been an essential aspect of Natural Language Processing (NLP) for a long time. Likewise, the recent advancement of Large Language…
Human-AI Coevolution
Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina +14
Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artif…
Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery
Martina Cinquini, Fosca Giannotti, Riccardo Guidotti
Synthetic data generation has been widely adopted in software testing, data privacy, imbalanced learning, and artificial intelligence explanation. In all such contexts, it is cruci…
Dense Hebbian neural networks: a replica symmetric picture of supervised learning
Elena Agliari, Linda Albanese, Francesco Alemanno +5
We consider dense, associative neural-networks trained by a teacher (i.e., with supervision) and we investigate their computational capabilities analytically, via statistical-mecha…
Dense Hebbian neural networks: a replica symmetric picture of unsupervised learning
Elena Agliari, Linda Albanese, Francesco Alemanno +5
We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via a statistical-mechanics approach, a…
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges
Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo +1
Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counterfactual Explanations (CE) provide counter-examples to overcome the transparency…
Measuring the Salad Bowl: Superdiversity on Twitter
Laura Pollacci, Alina Sirbu, Fosca Giannotti +1
Superdiversity refers to large cultural diversity in a population due to immigration. In this paper, we introduce a superdiversity index based on the changes in the emotional conte…
Explainable Deep Image Classifiers for Skin Lesion Diagnosis
Carlo Metta, Andrea Beretta, Riccardo Guidotti +4
A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems. Research in eXplainable Artificia…
Understanding peacefulness through the world news
Vasiliki Voukelatou, Ioanna Miliou, Fosca Giannotti +1
Peacefulness is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and pea…
Characterising different communities of Twitter users: Migrants and natives
Jisu Kim, Alina Sîrbu, Fosca Giannotti +1
Today, many users are actively using Twitter to express their opinions and to share information. Thanks to the availability of the data, researchers have studied behaviours and soc…
Benchmarking and Survey of Explanation Methods for Black Box Models
Francesco Bodria, Fosca Giannotti, Riccardo Guidotti +3
The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions. Re…
Home and destination attachment: study of cultural integration on Twitter
Jisu Kim, Alina Sîrbu, Giulio Rossetti +2
The cultural integration of immigrants conditions their overall socio-economic integration as well as natives' attitudes towards globalisation in general and immigration in particu…
GLocalX -- From Local to Global Explanations of Black Box AI Models
Mattia Setzu, Riccardo Guidotti, Anna Monreale +3
Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly…
Predicting seasonal influenza using supermarket retail records
Ioanna Miliou, Xinyue Xiong, Salvatore Rinzivillo +5
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real…
The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy
Paolo Cintia, Luca Pappalardo, Salvatore Rinzivillo +29
In 2020, countries affected by the COVID-19 pandemic implemented various non-pharmaceutical interventions to contrast the spread of the virus and its impact on their healthcare sys…
Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown
Pietro Bonato, Paolo Cintia, Francesco Fabbri +12
Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the e…
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…
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…