Algorithms are marketed as neutral, but their outputs mirror the biases of their training data and objectives. This episode investigates risk scores in courts, résumé filters in hiring, and recommendation engines in media—systems that appear objective yet quietly perpetuate inequity. The problem is not intent but opacity. Decisions dressed in statistical rigor command deference, even when wrong. Without transparency, appeal, and accountability, algorithmic judgment becomes a new disguise for old prejudices.

Selenius Media & The Artificial Laboratory