A hands-on, research-focused course exploring how large language models (LLMs) can inherit unfair biases—and what you can do to detect and mitigate these issues. Participants will replicate real bias-auditing methods from studies like the “Silicon Ceiling,” run simple statistical tests to confirm if differences are random or meaningful, and build an interactive mini tool that reveals whether LLM responses change when certain variables (like names) are tweaked. By the end, you’ll understand why bias emerges, learn how to measure it, and deploy a small-scale “bias auditing” web app.