Episode 44 of The Data Science Podcast with Fexingo dives into Shapley Values, a game-theory approach to explaining individual model predictions. Lucas and Luna break down how this method works, why it's more principled than feature importance rankings, and where it falls short in practice. They walk through a concrete example from credit scoring, explaining how a single applicant's denial can be attributed to specific factors like income, age, or zip code — and why that transparency matters under regulations like the EU AI Act. The conversation also touches on computational cost, alternative methods like LIME, and what data scientists should watch out for when deploying explanations at scale.

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