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.
ql4facct is a software for replicating experiments concerning the evaluation of estimators of classifier "fairness".
Educational kits for civil society, industry and policy. Easy-to-understand resources explaining AI bias and fairness. Designed for anyone—no technical background required. Practical case studies showing real-world examples.
An encyclopedia of the major scientific and technical terms related to Trustworthy Artificial Intelligence This book (to be consolidated in the second phase of the project) represents the first period deliverable of the TAILOR project, providing an enc...
Video recordings of the 1st TAILOR Summer School, which was delivered in an online form on 23-24 September, 2021.
We study fairness and find that it is not value-free, as it has serious implications in terms of who will be affected by a fairness intervention.