Week 16: Bias, Stereotypes, and Representation
Welcome to Week 16 of the AI Literacy Course. Imagine asking an AI system to write several stories about a successful company. In most stories, men become senior managers while women become assistants or carers. One story would be an example. If the same role pattern appears repeatedly under comparable conditions, it may indicate a pattern worth investigating. The central rule is: Notice the pattern, compare carefully, limit the conclusion, and assign responsibility for action. Learning goals By the end of this lesson, you should be able to: distinguish bias, stereotypes, representation, and unfair outcomes; distinguish representational harm from allocative harm; identify where bias can enter an AI-supported process; run a small controlled representation check; recognise proxy variables and combined effects; interpret findings without making claims the evidence cannot support; recommend an action and identify who is responsible for follow-up. 1. Bias is more than offensive langua...