Interpretability is often pointed out as a key requirement for trustworthy machine learning.
However, learning and releasing models that are inherently interpretable leaks information …

In this paper, we present an information-theoretic perspective to group fairness trade-offs in
federated learning (FL) with respect to sensitive attributes, such as gender, race, etc …

Data sharing in the European Union (EU) has gained new momentum, among others for
machine learning (ML) and artificial intelligence (AI) training purposes. By enabling models' …

The protection of sensitive data becomes more vital, as data increases in value and potency.
Furthermore, the pressure increases from regulators and society on model developers to …

Large Language Models (LLMs) rely on the contextual information embedded in examples/
demonstrations to perform in-context learning (ICL). To mitigate the risk of LLMs potentially …

This paper introduces a novel information-theoretic perspective on the relationship between
prominent group fairness notions in machine learning, namely statistical parity, equalized …

We introduce an optimization-based reconstruction attack capable of completely or near-
completely reconstructing a dataset utilized for training a random forest. Notably, our …

Machine learning techniques are increasingly used for high-stakes decision making, such
as college admissions, loan attribution or recidivism prediction. It is thus crucial to ensure …

The development of machine learning raised some concerns about both privacy and
fairness in modern society. This integration brings ethical considerations and societal …