When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

Deep neural networks are hegemonic approaches to many machine learning areas,
including natural language processing (NLP). Thanks to the availability of large corpora …

While algorithm audits are growing rapidly in commonality and public importance, relatively
little scholarly work has gone toward synthesizing prior work and strategizing future research …

Machine learning tools have been deployed in various contexts to support human decision-
making, in the hope that human-algorithm collaboration can improve decision quality …

The study of law and information technology comes with an inherent contradiction in that
while technology develops rapidly and embraces notions such as internationalization and …

Recently there are increasing concerns about the fairness of Artificial Intelligence (AI) in real-
world applications such as computer vision and recommendations. For example, recognition …

Recent evolution in the field of data science has revealed the potential utility of machine
learning (ML) applied to criminal justice. Hence, the literature focused on finding better …

Nowadays, there is an increasing concern in machine learning about the causes underlying
unfair decision making, that is, algorithmic decisions discriminating some groups over …

In an effort to regulate Machine Learning-driven (ML) systems, current auditing processes
mostly focus on detecting harmful algorithmic biases. While these strategies have proven to …