Not all AI bias shows up as broken code or obvious red flags. Often, it looks like “business as usual”, until a pattern of unfair outcomes quietly emerges. That’s the danger of fairness traps: they start with logic that seems reasonable—trust the numbers, follow best practices, move fast—and end with HR decisions that leave people behind.

In this lesson, you’ll learn to spot five common fairness traps that show up in AI-supported hiring, performance, and engagement tools. Each one feels efficient on the surface but creates blind spots underneath. You’ll build the reflexes to catch these traps early, and bring context and judgment back into the loop before small missteps turn into scaled harm.

Learning objectives

By the end of this lesson, you’ll be able to:

• Explain why fairness failures often happen even with good intentions
• Identify five common fairness traps in AI-supported HR workflows
• Recognize early signals that a trap may be forming
• Distinguish between outcomes that look “fair on paper” and those that are fair in practice
• Apply practical interventions to catch and correct fairness issues before they scale

This lesson sets you up for Lesson 2.4: The ART Framework, where you’ll learn a fast, repeatable way to pause, assess, and act responsibly when AI is in play.