Visual Explanations for Machine Learning Models

As ML models are increasingly deployed in high-stakes domains — medicine, law, urban planning — the need for interpretable explanations grows. This project builds visual interfaces that expose model internals to domain experts who need to trust, challenge, or correct automated decisions.

Our approach combines dimensionality reduction, attention visualization, and contrastive explanation techniques into unified interactive environments.