Federated learning is a decentralized machine learning technique that aggregates partial models trained by a set of clients on their own private data to obtain a global model....

A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as...

Federated learning (FL) is an alternative to centralized machine learning (ML) that builds a model across multiple decentralized edge devices (a.k.a. workers) that own the...

The cognitive constraints that humans exhibit in their social interactions have been extensively studied by anthropologists, who have highlighted their regularities across...

The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web...

Studying migration using traditional data has some limitations. To date, there have been several studies proposing innovative methodologies to measure migration stocks and...

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...

Academic migration is the change of host institution by a researcher, typically aimed at achieving a stronger research profile. Scientific features such as the number of...

ML systems have become an essential tool for experts of many domains, data scientists and researchers, allowing them to find answers to many complex business questions...

A recent trend in algorithm design consists of augmenting classic data structures with machine learning models, which are better suited to reveal and exploit patterns and trends...

Peacefulness is a principal dimension of well-being, and its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital...

Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of data that describe all the spatio-temporal events that occur in each...

Stance classification can be a powerful tool for understanding whether and which users believe in online rumours. The task aims to automatically predict the stance of replies...

The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language...

We present an approach to explain the decisions of black-box image classifiers through synthetic exemplar and counter-exemplar learnt in the latent feature space. Our...

We present xspells, a model-agnostic local approach for explaining the decisions of a black box model for sentiment classification of short texts. The explanations provided...

Impact assessments have received particular attention on both sides of the Atlantic as a tool for implementing algorithmic accountability. The aim of this paper is to address...

An ever-increasing number of decisions affecting our lives are made by algorithms. For this reason, algorithmic transparency is becoming a pressing need: automated decisions...

Mobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the...

We present a method to explain the decisions of black box models for time series classification. The explanation consists of factual and counterfactual shapelet-based rules...