AI bias and fairness are the focus of this Grade 7 Computer Science worksheet, helping students examine why artificial intelligence systems can produce unfair outcomes and how training data and design choices influence those results. Students connect the ideas of bias, fairness, representation, and evaluation while considering practical ways AI decisions can be assessed and improved.

Understand where unfair AI outcomes can begin

Students first define bias and fairness in the context of artificial intelligence systems. They then examine how representation within training data can affect the outcomes an AI system produces. This builds an important connection between the information used by a system and the decisions or predictions that may follow.

The lesson asks students to identify examples of biased data and design choices that can contribute to unfair results. Rather than treating AI bias as an isolated vocabulary concept, students reason about how choices made when collecting data or designing a system can influence who is represented and how different outcomes may occur.

Move from identifying bias to improving fairness

Students also consider ways to evaluate and improve fairness in AI. They describe at least one approach for checking whether outcomes are fair and think about how problems related to representation or design could be addressed. This gives students a foundation for discussing AI systems critically instead of assuming that automated decisions are automatically neutral.

How students demonstrate their understanding

Practice includes multiple-choice questions, true-or-false statements, short-answer responses, a dialogue, matching, a cloze passage, a labeled diagram, a reading passage, and an essay. These formats allow students to define key concepts, identify examples of bias, interpret fairness issues, and explain how AI outcomes could be evaluated or improved.

Teacher support included

• The Bias and Fairness in AI Decisions student worksheet
• A separate Teacher Guide PDF with Learning Objectives
• Teaching Tips for introducing and reinforcing the topic
• Discussion Questions for extending classroom conversations
• Common Mistakes to help teachers address misconceptions

The teacher guide supports discussion of how training data representation and design decisions can influence AI outcomes while providing prompts for exploring ways fairness can be evaluated and improved.

Use it within an artificial intelligence unit

This AI bias worksheet can be used during an artificial intelligence unit, for independent practice, homework, or review. By connecting training data, representation, biased design choices, unfair outcomes, and fairness evaluation, the resource gives Grade 7 students structured practice thinking critically about how AI systems make decisions. :contentReference[oaicite:0]{index=0}