Who gets the job interview, the loan, the diagnosis, the second look from police — AI already helps decide. This course takes you from true stories of algorithms gone wrong to the working skills of responsible AI: diagnosing where bias enters a system, understanding why "fair" has no single definition, auditing any AI tool in an afternoon, and a six-practice code for your own everyday AI use. Built on real cases, hands-on AI practice, and zero hype.
AI ethics isn't a philosophy seminar — it's the study of real decisions that already touch real lives: who gets a job interview, a loan, a diagnosis, or a second look from the police. This module opens with true stories, shows you where in an AI system ethics actually lives, and hands you a five-question lens you'll use for the rest of the course.
Bias doesn't sneak into AI systems through one door — it has keys to five of them. This module teaches you to recognize each entry point: the data that mirrors history, the proxies that smuggle it, the humans who label it, and the feedback loops that lock it in. By the end you'll diagnose bias in scenarios the way a mechanic hears an engine.
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Here's where it gets genuinely hard — and genuinely interesting. "Fair" turns out to have several precise definitions that can't all be true at once. Privacy turns out to be about purpose, not secrecy. And accountability turns out to be a chain that's only as strong as its most weasel-worded link. This module gives you the grown-up versions of all three.
Enough diagnosis — time for tools you'll actually use. This closing module turns everything you've learned into working habits: a personal code for everyday AI use, a lightweight audit you can run on any tool or workflow in an afternoon, and an honest look at the disagreements that will shape the next decade. You'll leave with a checklist, not just a viewpoint.