The math equation deciding whether you get a mortgage, a job interview, or adequate medical care might be actively prejudiced against you — and nobody programmed it to be. This episode explores one of the most urgent problems in modern technology: how algorithms trained on historical data systematically inherit and amplify the biases of the humans who created that data.

We break down the mechanics of algorithmic bias from the ground up, starting with a counterintuitive truth: computers aren't objective. Machine learning models learn patterns from training data, and when that data reflects decades of discriminatory lending practices, biased hiring decisions, or unequal healthcare access, the algorithm faithfully reproduces those patterns at scale — faster, more efficiently, and with a veneer of mathematical legitimacy that makes the bias har ...