An Analytic Road Map to Examine Efficiency and Fairness in Automated and Opaque Decision Making We are currently witnessing a sharp rise in the use of algorithmic...

Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a...

Artificial intelligence, and machine learning (ML) in particular, is being used for different purposes that are critical for human life. To avoid an algorithm-based...

Algorithms form an increasingly important part of our daily lives, even if we are often unaware of it. They are enormously useful in many different ways. They facilitate the...

Our society is being shaped in a non-negligible way by the technological advances of recent years, especially in information and communications technologies (ICTs). The...

The emergency of the Coronavirus imposes a cultural debate on the balancing of rights, freedoms and social responsibilities, finalized to the protection of individual and...

Informational self-determination refers to the right or ability of individuals to exercise personal control over the collection, use and disclosure of their personal data by...

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...

Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached...

The speed of development in Big Data and associated phenomena, such as social media, has surpassed the capacity of the average consumer to understand his or her actions and...

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...

In the age of Big Data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance...

Machine learning algorithms can now identify patterns and correlations in (big) datasets and predict outcomes based on the identified patterns and correlations. They can then...

Society is increasingly relying on data-driven predictive models for automated decision making. This is not by design, but due to the nature and noisiness of observational...

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of...