Ethics Bias Check

Algorithms that treat everyone the same can still discriminate. A ranking that optimises for engagement may systematically deprioritise certain groups. A model trained on historical data may encode historical injustice. A feature that works well on average may fail badly for users who aren't the implicit default.

This check surfaces those patterns before they ship.

Your Process

Step 1: Define the system What is the algorithm, model, or automated decision? What is its input? What is its output? Who does it make decisions about? What happens to people based on its output?