The Visual Bias Mitigator is an open-source framework designed to empower researchers in the field of bias mitigation in computer vision. This codebase provides a comprehensive environment where users can easily implement, run, and evaluate existing visual bias mitigation methods.
In this tutorial, we explore how algorithmic fairness can be quantified and explored, and what one may need to pay attention to. We do this through the FairBench library, which is tailored to practical use in many pipelines (regardless of whether they focus on numpy, pandas, torch,tf, etc.). Fairbench serves as a comprehensive AI fairness exploration framework, offering tools to…
The tutorial series offers a hands-on guide to fairness-aware machine learning for beginners in Fair-ML. It emphasises the urgency for equitable, transparent, and accountable AI/ML systems given their influence on important decisions. It is designed to empower data scientists, researchers, and developers to create more equitable and trustworthy AI systems…
