Data Scientists Use Counterfactual Explanations for Model Debugging

What this episode covers

Episode 97 dives into counterfactual explanations — the 'what if' tools helping data scientists debug models and build stakeholder trust. Lucas and Luna walk through a concrete example: a credit-approval model that rejected a loan applicant, and how a counterfactual explanation revealed a single feature — years at current address — was the deciding factor. They discuss practical implementation using the DiCE library, trade-offs between feasibility and diversity of counterfactuals, and why this approach beats traditional feature importance for non-technical audiences. The episode closes with a reflection on how counterfactuals are becoming a regulatory and ethical baseline in high-stakes ML deployments. #CounterfactualExplanations #ModelDebugging #XAI #MachineLearning #DataScience #DiCE #FeatureImportance #CreditModeling #AIEthics #Interpretability #Python #CausalReasoning #TrustworthyAI #RegulatoryCompliance #Technology #DataSciencePodcast #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

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