Much attention has focused on algorithmic audits and impact assessments to hold
developers and users of algorithmic systems accountable. But existing algorithmic …

Automated decision systems are increasingly used to take consequential decisions in
problems such as job hiring and loan granting with the hope of replacing subjective human …

We theoretically study the impact of differential privacy on fairness in classification. We prove
that, given a class of models, popular group fairness measures are pointwise Lipschitz …

An emerging concern in algorithmic fairness is the tension with privacy interests. Data
minimization can restrict access to protected attributes, such as race and ethnicity, for bias …

Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have
emerged to address these issues. This paradigm relies on generative AI models to generate …

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 …

In recent years, a growing body of work has emerged on how to learn machine learning
models under fairness constraints, often expressed with respect to some sensitive attributes …

Fair classification and fair representation learning are two important problems in supervised
and unsupervised fair machine learning, respectively. Fair classification asks for a classifier …

The performance cost of differential privacy has, for some applications, been shown to be
higher for minority groups; fairness, conversely, has been shown to disproportionally …

Clinical NLP tasks such as mental health assessment from text, must take social constraints
into account-the performance maximization must be constrained by the utmost importance of …