Is a truly unbiased algorithm mathematically impossible? In this episode, we explore the provocative idea that to learn is, by definition, to become biased. We dive into the No Free Lunch theorem, which proves that an algorithm with no assumptions is no better than random chance, and the Bias-Variance Tradeoff, which reveals that a model without bias can’t generalize—it just records noise.

We also pull back the curtain on "your algorithm," reframing it as a "proxy desire engine" optimized for platform engagement rather than your best interests. Finally, we discuss why the standard for AI ethics must shift from "justified" choices to a more rigorous triad: explained, transparent, and fair. Join us for a deep dive into the mathematical necessity of bias and w ...