How Data Scientists Use Counterfactual Explanations for Model Interpretability
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
In this episode, Lucas and Luna dive into counterfactual explanations—a technique that helps data scientists make machine learning models more interpretable by showing what would need to change for a prediction to flip. They walk through a concrete example from a credit approval model at a European fintech, where a loan denial was explained by showing that increasing income by $5,000 would have changed the outcome. The hosts discuss how counterfactuals differ from traditional feature importance, the trade-offs between proximity and plausibility, and the emerging regulatory push under the EU AI Act that makes this approach increasingly relevant. They also touch on open-source tools like What-If Tool and ALIBI that practitioners can use today. No fluff—just a focused look at one practical technique for building trust in black-box models. #CounterfactualExplanations #ExplainableAI #XAI #ModelInterpretability #MachineLearning #DataScience #AIEthics #CreditApproval #EUAIAct #WhatIfTool #ALIBI #LIME #SHAP #Technology #Podcast #FexingoBusiness #BusinessPodcast #DataDriven Keep every episode free: buymeacoffee.com/fexingo
Embed this episode
No transcript yet.