Adversarial Robustness of Variational Autoencoders across Intersectional Subgroups. read more of Subgroup-Robustness

Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study. read more of FairAugment

Counterfactual explanations for tabular data. read more of TabCF

Multi-attribute, Multi-objective, Multi-definition aware fair classifiers. read more of mmm-fair

This tutorial series offers a hands-on guide to fairness-aware machine learning that targets beginners in Fair-ML. read more of Fairness-Aware ML Tutorial Series

An investigation into the fairness and bias implications of multimodal fusion techniques in AI-driven recruitment systems using the FairCVdb dataset, examining how different fusion strategies influence bias and impact fairness in hiring decisions. read more of Exploring Bias and Fusion in Multimodal AI Recruitment with FairCVdb

EnvClus* is an unsupervised data-driven framework that forecasts the most probable realistic and smooth trajectory from a given query position of a vessel (entire route or underway forecasting) towards its destination port. read more of EnvClus*: Extracting Common Pathways for Effective Vessel Trajectory Forecasting