Responsible AI, Bias, Security, and Social Impact
Lesson 2: Bias Is About People and Outcomes
Lesson Objectives
By the end of this lesson, students should be able to:
- Explain bias in practical terms.
- Review output for unfair assumptions.
- Add fairness checks to an AI workflow.
Lesson Content
Bias is not only a technical word. In everyday use, bias means an AI-assisted process may treat people or groups unfairly, ignore important context, or repeat patterns that should not be repeated.
Bias can enter through:
- Data that leaves out some people.
- Examples that reflect old unfair decisions.
- Prompts that frame one group negatively.
- Reviewers who accept confident output too quickly.
- Policies that use AI output without appeal or correction.
Students should ask: Who might be misread by this system? Who is missing from the examples? What would a person need to challenge or correct the result?
Enroll to continue this lesson.
The preview above shows the lesson objectives and opening lesson content. Enroll to view the full lesson, complete the practice work, and take the lesson quiz.