Lesson 1: Can Data Be Unfair?

About 30 minutes — Discussion-based lesson

What You Will Learn

This lesson covers:

The myth of objective data: every dataset reflects human choices
How collection methods can exclude certain groups
How analysis choices can amplify inequality
Real examples: biased hiring algorithms, unfair school assessments

The myth of objective data: every dataset reflects human choices

This section covers the key ideas about the myth of objective data: every dataset reflects human choices. Discuss with your group or family and explore the concepts together.

How collection methods can exclude certain groups

This section covers the key ideas about how collection methods can exclude certain groups. Discuss with your group or family and explore the concepts together.

How analysis choices can amplify inequality

This section covers the key ideas about how analysis choices can amplify inequality. Discuss with your group or family and explore the concepts together.

Real examples: biased hiring algorithms, unfair school assessments

This section covers the key ideas about real examples: biased hiring algorithms, unfair school assessments. Discuss with your group or family and explore the concepts together.

Check Your Understanding

1. Is data always objective?

Answer: No! Every dataset reflects human choices: what to measure, who to include, how to categorize. These choices can introduce bias, even unintentionally.

2. How can data collection be unfair?

Answer: By excluding certain groups (only surveying English speakers), using biased categories (forcing people into groups that do not fit), or measuring things that disadvantage some people.

3. Can a computer be biased?

Answer: Yes. Algorithms learn from historical data. If that data reflects past discrimination, the algorithm will perpetuate that discrimination automatically.

4. Why does this matter for 5th graders?

Answer: Because you will encounter data-driven decisions your whole life — school admissions, job applications, medical treatment. Understanding fairness helps you advocate for yourself and others.

Key Takeaways

The myth of objective data: every dataset reflects human choices
How collection methods can exclude certain groups
How analysis choices can amplify inequality
Real examples: biased hiring algorithms, unfair school assessments

Ready for More?

Next Lesson

Continue to Lesson 2: Who Is Missing From the Data?

Start Lesson 2

Session Home

Review all lessons and materials.

Back to Session Home