Explainbench is a Python toolkit that makes powerful ML interpretability techniques like SHAP, LIME, counterfactuals, and global surrogate models accessible and usable — especially for high-stakes, public-sector applications.
- Unified Interface for SHAP, LIME, and DiCE
- Fairness & Explainability Metrics (Disparate Impact, Fidelity, Consistency)
- Preloaded Datasets (COMPAS, Adult Income, etc.)
- Interactive Visualizations with Streamlit and Plotly
- Notebook Examples for quick understanding and classroom use
As ML systems are increasingly used in criminal justice, healthcare, and finance, it's crucial that we can explain, audit, and challenge their decisions. Explainbench provides transparent tools for evaluating black-box models in real-world, socially relevant contexts.
pip install explainbench