no code implementations • 9 Feb 2024 • Clara Punzi, Roberto Pellungrini, Mattia Setzu, Fosca Giannotti, Dino Pedreschi
Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions.
no code implementations • 23 Jun 2023 • Dino Pedreschi, Luca Pappalardo, Ricardo Baeza-Yates, Albert-Laszlo Barabasi, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, Janos Kertesz, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex Sandy Pentland, John Shawe-Taylor, Alessandro Vespignani
In order to understand the impact of AI on socio-technical systems and design next-generation AIs that team with humans to help overcome societal problems rather than exacerbate them, we propose to build the foundations of Social AI at the intersection of Complex Systems, Network Science and AI.
no code implementations • 25 Nov 2022 • Elena Agliari, Linda Albanese, Francesco Alemanno, Andrea Alessandrelli, Adriano Barra, Fosca Giannotti, Daniele Lotito, Dino Pedreschi
We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via a statistical-mechanics approach, and numerically, via Monte Carlo simulations.
no code implementations • 25 Nov 2022 • Elena Agliari, Linda Albanese, Francesco Alemanno, Andrea Alessandrelli, Adriano Barra, Fosca Giannotti, Daniele Lotito, Dino Pedreschi
We consider dense, associative neural-networks trained by a teacher (i. e., with supervision) and we investigate their computational capabilities analytically, via statistical-mechanics of spin glasses, and numerically, via Monte Carlo simulations.
1 code implementation • 25 Feb 2021 • Francesco Bodria, Fosca Giannotti, Riccardo Guidotti, Francesca Naretto, Dino Pedreschi, Salvatore Rinzivillo
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.
1 code implementation • 19 Jan 2021 • Mattia Setzu, Riccardo Guidotti, Anna Monreale, Franco Turini, Dino Pedreschi, Fosca Giannotti
Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other.
1 code implementation • 8 Dec 2020 • Ioanna Miliou, Xinyue Xiong, Salvatore Rinzivillo, Qian Zhang, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi, Alessandro Vespignani
In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting.
no code implementations • 8 Nov 2020 • Cecilia Panigutti, Alan Perotti, Andrè Panisson, Paolo Bajardi, Dino Pedreschi
The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare.
no code implementations • 27 Jan 2020 • Riccardo Guidotti, Anna Monreale, Stan Matwin, Dino Pedreschi
We present an approach to explain the decisions of black box models for image classification.
no code implementations • 31 May 2019 • Margarita Baltakienė, Kęstutis Baltakys, Juho Kanniainen, Dino Pedreschi, Fabrizio Lillo
The complex networks approach has been gaining popularity in analysing investor behaviour and stock markets, but within this approach, initial public offerings (IPO) have barely been explored.
no code implementations • 26 Jun 2018 • Dino Pedreschi, Fosca Giannotti, Riccardo Guidotti, Anna Monreale, Luca Pappalardo, Salvatore Ruggieri, Franco Turini
We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions: (i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation; (ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.
1 code implementation • 28 May 2018 • Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, Fosca Giannotti
Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome.
1 code implementation • 14 Feb 2018 • Luca Pappalardo, Paolo Cintia, Paolo Ferragina, Emanuele Massucco, Dino Pedreschi, Fosca Giannotti
The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e. g., tackles, passes, shots, etc.).
no code implementations • 6 Feb 2018 • Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Dino Pedreschi, Fosca Giannotti
The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation.
1 code implementation • 15 Dec 2017 • Giulio Rossetti, Letizia Milli, Salvatore Rinzivillo, Alina Sirbu, Fosca Giannotti, Dino Pedreschi
Nowadays the analysis of dynamics of and on networks represents a hot topic in the Social Network Analysis playground.
Social and Information Networks 05C85, 60J60, 90C35 G.2.2; F.2.1
no code implementations • 5 Dec 2017 • Luca Pappalardo, Paolo Cintia, Dino Pedreschi, Fosca Giannotti, Albert-Laszlo Barabasi
Humans are routinely asked to evaluate the performance of other individuals, separating success from failure and affecting outcomes from science to education and sports.
no code implementations • 12 Oct 2015 • Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis
Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems.