The anonymity set for an individual u, denoted ASu is the set of users that the adversary cannot distinguish from u. It can be seen as the size of the crowd into which the target u can blend.

privASS ≡ |ASu |

This metric counts the information items S disclosed by a system, e.g., the number of compromised users. However, this metric does not indicate the severity of a leak because it does not account for the
sensitivity of the leaked information.

The demographic disparity metric (DD) determines whether a facet has a larger proportion of the rejected outcomes in the dataset than of the accepted outcomes. In the binary case where there are two facets, men and women for example, that constitute the dat...

We propose a set of interrelated metrics, all based on the notion of AI output concentration, and the related Lorenz curve/Lorenz area under the curve, able to measure the Sustainability/robustness, Accuracy, Fairness/privacy, Explainability/accountability ...

The Empirical Likelihood-Based Fairness Auditing framework is a statistical method for auditing the fairness of machine learning (ML) models. ML models used in high-stakes decisions, such as predicting reoffending or selecting job candidates, often perform ...

Peer-induced fairness is a framework for auditing the fairness of algorithmic decisions, based on causal reasoning. The EU AI Act, which entered into force on 1 August 2024, requires high-risk AI applications to meet strict standards of transparency and fai...

The Fairness Disagreement Index (FDI) is a measure of how far different fairness metrics contradict each other when applied to the same machine learning (ML) model. It was introduced in the research paper "When Fairness Metrics Disagree", first published in...