# How Data Scientists Build Interpretable ML Models with SHAP Page: https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-build-interpretable-ml-models-with-shap Text version: https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-build-interpretable-ml-models-with-shap.md Podcast: [The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations](https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831) Published: 2026-07-10T08:58:30+00:00 Episode link: https://audio.fexingo.com/business/the-data-science-podcast/episode-0101.mp3 Audio file: https://audio.fexingo.com/business/the-data-science-podcast/episode-0101.mp3 Processing state: not_requested JSON: https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-build-interpretable-ml-models-with-shap Duration seconds: 732 ## Resource Lucas and Luna explore the practical use of SHAP (SHapley Additive exPlanations) for interpreting complex machine learning models. They walk through a real-world example: a credit risk model from a mid-sized European fintech that needed regulatory compliance under GDPR. Lucas explains how SHAP values decompose a prediction into feature contributions, and why game theory provides a principled foundation. Luna questions whether SHAP is always better than simpler alternatives like LIME, and they compare trade-offs in speed, consistency, and trust. The episode includes a concrete walkthrough of a single prediction breakdown, showing how a 32-year-old applicant with a thin credit file got denied because of a specific feature interaction. They also touch on open-source tools like the SHAP Python library and how one data team at Klarna uses SHAP summaries to communicate with non-technical stakeholders. No clickbait, just a clear look at one of the most widely adopted interpretability methods in the field today. #SHAP #InterpretableML #ExplainableAI #XAI #SHAPValues #GameTheory #FeatureImportance #ModelInterpretability #CreditRiskModeling #GDPR #LIME #Klarna #DataScience #Technology #MachineLearning #FexingoBusiness #BusinessPodcast #DataDriven Keep every episode free: buymeacoffee.com/fexingo ## Actions - request_transcript: `POST https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-build-interpretable-ml-models-with-shap/transcription-requests` — Idempotently request low-priority transcript generation for this episode. - read_markdown: `GET https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-build-interpretable-ml-models-with-shap.md` — Read the agent-friendly Markdown representation of this episode resource. A page view does not enqueue transcription. Agents should invoke `request_transcript` explicitly when they need this episode processed. ## Transcript Full transcripts are not published on public pages unless there is a clear rights basis.