AI Investigators · Grades 6-8Module 2 of 6
Bias in AI
Students examine how bias enters AI systems and explore real-world examples where AI has produced unfair or harmful outcomes.
See what you already know
3 quick questions before you start. Optional.
What you'll learn
- Explain what bias means and how it can get into an AI tool
- Name three places bias can enter: the data, the goal people choose, and how a tool is tested and used
- Recognize who can be harmed when an AI tool works better for some people than others
- Suggest practical ways to catch and reduce bias before a tool is used
Words to know
- bias
- A pattern that unfairly favors or works against some people or groups.
- training data
- The examples an AI tool learns from, like labeled photos, emails, or past decisions.
- algorithm
- A set of step-by-step instructions that a computer follows.
- feedback loop
- When a tool's results become its future data, so a pattern keeps repeating and can grow.
- audit
- A careful check of a system, often by outside experts, to see whether it works fairly for everyone.