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 …

Machine Learning is increasingly employed to make consequential decisions for humans. In
response to the ethical issues that may ensue, an active area of research in ML has been …

As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …

Recent works have shown that selecting an optimal model architecture suited to the
differential privacy setting is necessary to achieve the best possible utility for a given privacy …

The recent literature on fair Machine Learning manifests that the choice of fairness
constraints must be driven by the utilities of the population. However, virtually all previous …

In this paper, we cast fair machine learning as invariant machine learning. We first formulate
a version of individual fairness that enforces invariance on certain sensitive sets. We then …

Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

In recent years, there has been increasing interest in causal reasoning for designing fair
decision-making systems due to its compatibility with legal frameworks, interpretability for …

Unintended biases in machine learning (ML) models are among the major concerns that
must be addressed to maintain public trust in ML. In this paper, we address process fairness …

Fairness and robustness are critical elements of Trustworthy AI that need to be addressed
together. Fairness is about learning an unbiased model while robustness is about learning …