AI ethics usually fails in one of two ways: it either becomes pure philosophy that never touches code, or it becomes pure compliance that never touches what people actually do. The only version that survives contact with reality is a triangulation: 1. Philosophy gives you the “why” and the non-negotiables.

A familiar story in AI ethics goes like this: 1. A model is trained on historical data. 2. The model reproduces historical inequity. 3. We fix the data, the model, or the evaluation. That story is true—but incomplete. Here’s the thesis: bias in AI systems is often an

Open any ethics deck about AI recommendations and you’ll see the usual suspects: bias, misinformation, radicalization, echo chambers. Then you’ll see the usual solution: “align the algorithm.” That framing flatters us. It suggests the algorithm is confused about values and needs guidance. But recommendation systems are rarely confused.

It’s easy to build an AI that sounds ethical. It’s harder to build a business that stays ethical once the quarterly dashboard starts blinking red. Most “AI ethics” debates pretend the hard part is philosophy (values), or engineering (model behavior), or governance (rules). In practice, the hard part