Where explainability methods reach their limits
Explainability tools are helpful but have boundaries. Data scientists should recognise when feature importance, LIME/SHAP or counterfactuals mislead because of feature correlation, model nonlinearity or dataset shift. Discuss practical checks to validate explanations and how to present uncertainty to stakeholders.
Explore communitiesExplore live jobs