Existing regulations often prohibit model developers from accessing protected attributes
(gender, race, etc.) during training. This leads to scenarios where fairness assessments …

This paper presents a systematic review of the literature on evaluation criteria for
Trustworthy Artificial Intelligence (TAI), with a focus on the seven EU principles of TAI. This …

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 …

A sizable proportion of deployed machine learning models make their decisions in a black-
box manner. Such decision-making procedures are susceptible to intrinsic biases, which …

Before deploying a black-box model in high-stakes problems, it is important to evaluate the
model's performance on sensitive subpopulations. For example, in a recidivism prediction …

Quantifying disparities, that is differences in outcomes among population groups, is an
important task in public health, economics, and increasingly in machine learning. In this …

Abstract Generative Adversarial Networks (GANs) are notoriously difficult to train especially
for complex distributions and with limited data. This has driven the need for interpretable …

Responsible use of machine learning requires models to be audited for undesirable
properties. While a body of work has proposed using explanations for auditing, how to do so …

Existing work in fairness auditing assumes that each audit is performed independently. In
this paper, we consider multiple agents working together, each auditing the same platform …

Artificial intelligence (AI) is increasingly intervening in our lives, raising widespread concern
about its unintended and undeclared side effects. These developments have brought …