Our remarks focus on two main issues: 1) providing operational tools to link the ethics and the legal dimension of a Trustworthy AI avoiding risks of ethics washing; 2) the...

Starting from an analysis of the EU Reg. n. 2016/679 on General Data Protection Regulation (GDPR), the Author deals with the opportunity to translate the current strategies on...

This webinar, which took place on 6 July 2021, focused on the interplay between legal data and data science. The webinar, entitled ‘Legal Materials as Big Data: (algo)Rithms to...

This webinar, which took place on 10 November 2020, was aimed at exploring the theme of data protection and intellectual property issues in platforms. The first speaker was...

Privlib is a Python software package to manage privacy risk and discrimination in tabular and sequential data. It comprises methods to assess privacy risk (PRUDEnce) and...

This dataset contains the result of a systematic mapping study conducted to analyse how the issue of gender bias in academic promotions has been addressed by the literature....

MANILA is a low-code web application to support the specification and execution of machine learning fairness evaluations. In particular, through MANILA it is possible to...

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...

Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate...

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud...

Artificial intelligence (AI) is being deployed in missions that are increasingly critical for human life. To build trust in AI and avoid an algorithm-based authoritarian...

Models for understanding and holding systems accountable have long rested upon ideals and logics of transparency. Being able to see a system is sometimes equated with being able...

Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial...

"Big Data" and data-mined inferences are affecting more and more of our lives, and concerns about their possible discriminatory effects are growing. Methods for...

Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time....

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would...

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...

The role of decisions made by machine learning algorithms in our lives is ever increasing. In reaction to this phenomenon, the European General Data Protection Regulation...