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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?

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