Algorithmic Political Bias

This paper examines how algorithms — not just human bias — actively manufacture political polarization. Recommendation and clustering systems sort users into “epistemic bubbles” that reinforce existing beliefs and can radicalize people who didn’t start out polarized, while two persistent myths (the “virtuality fallacy” that online harm isn’t fully “real,” and the “neutrality fallacy” that algorithms are objective) let platforms avoid accountability.

I evaluate a concrete legislative response — H.R. 4624, a U.S. House bill that would have required transparency for algorithmic processes using personal data and created federal oversight with real civil penalties — alongside longer-term AI governance proposals for keeping systems aligned and auditable as they scale.

My takeaway: technical fixes — better classifiers, better moderation models — only go so far. Without enforceable transparency requirements and real consequences for harm, the incentive for platforms to address algorithmic political bias stays weak, which is exactly why I see AI ethics and policy as inseparable from the modeling work itself.