Episode 35 of The Data Science Podcast tackles the messy reality of algorithmic fairness. Lucas and Luna explore why standard fairness metrics can conflict, using the real-world example of a lending model that passed statistical parity tests but failed an individual fairness audit. They discuss the Impossibility Theorem of Fairness, how researchers at MIT measured bias in commercial facial recognition, and why fairness is a product decision, not a mathematical one. The hosts also share practical steps data scientists can take today: documenting design choices, running disaggregated evaluations, and building simple guardrail dashboards. No platitudes, just the trade-offs and tools that practitioners actually use.

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