Visual Explanations for Machine Learning Models
Interactive visualization techniques that help domain experts understand, audit, and debug machine learning models without requiring ML expertise.
Lead: Felipe Adeildo
As ML models reach high-stakes domains such as medicine, law, and urban planning, the need for interpretable explanations grows.
Our approach combines dimensionality reduction, attention visualization, and contrastive explanation techniques into unified interactive environments.