Building on What You Already Know

This isn't the first time this app has touched on ethics and safety. AI Fundamentals introduced bias — why skewed training data produces skewed outputs — and Using AI Tools Well covered personal safety: what sensitive information to keep out of a general-purpose AI tool, and how to spot an AI-assisted scam.

Those were both real, useful starting points — but both were written from the user's side of the table: what to watch out for as someone using an AI tool day to day. This track flips to the other side of that table entirely: the deeper mechanics of how bias actually forms, how fairness gets defined and contested, and who's accountable when a system built by an organization causes real harm.

Nothing from those earlier tracks gets contradicted here — this track assumes you already know that skewed data produces skewed outputs, and that some information should stay private. It picks up right where those left off and goes further, into the questions a builder, a deployer, or an informed citizen needs to be able to answer, not just a careful individual user.

How does this track relate to AI Fundamentals' bias content and Using AI Tools Well's safety content?

AI Fundamentals and Using AI Tools Well were both written from the ____'s side of the table; this track shifts to the builder/deployer/citizen side.

This track assumes you already understand that skewed training data can produce skewed outputs, and builds further from there.