With widespread use of machine learning methods in numerous domains involving humans,
several studies have raised questions about the potential for unfairness towards certain …

Much of the previous machine learning (ML) fairness literature assumes that protected
features such as race and sex are present in the dataset, and relies upon them to mitigate …

There are many competing definitions of what statistical properties make a machine learning
model fair. Unfortunately, research has shown that some key properties are mutually …

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 …

As machine learning becomes prevalent in our daily lives involving a widening array of
applications such as medicine, finance, job hiring and criminal justice, one morally & legally …

Machine learning is being integrated into a growing number of critical systems with far-
reaching impacts on society. Unexpected behaviour and unfair decision processes are …

Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and
trustworthy Machine Learning (ML). Each objective has been independently studied in the …

As machine learning algorithms grow in popularity and diversify to many industries, ethical
and legal concerns regarding their fairness have become increasingly relevant. We explore …

As automated data analysis supplements, and even replaces, human supervision in
decision making, there are growing societal concerns about potential unfairness of these …

We present a framework that allows to certify the fairness degree of a model based on an
interactive and privacy-preserving test. The framework verifies any trained model, regardless …