Bias and fairness in artificial intelligence are the focus of this Grade 9 Computer Science worksheet, helping students examine how training data, representation, and evaluation can influence AI outcomes. Students learn that unequal results can occur even when an algorithm does not contain explicit rules treating groups differently. :contentReference[oaicite:0]{index=0}

Explore where AI bias can begin

The lesson connects fairness directly to the data used to train an AI system. Students examine how incomplete or unbalanced training data can cause a model to learn patterns that work better for some groups than others. Representation is introduced as a way to describe how well different groups are included in the training set.

Students also consider why evaluating only the code is not enough. Fairness requires examining outcomes across groups and looking for differences in accuracy, error rates, or other decision results. This gives students a practical framework for discussing responsible AI without treating fairness as an abstract idea.

Fairness concepts students apply

• Defining bias, fairness, and representation in AI contexts
• Explaining how training data choices can contribute to unequal outcomes
• Identifying incomplete, unbalanced, or poorly representative datasets
• Comparing AI outcomes across groups to identify possible fairness concerns
• Describing ways to reduce bias through improved data collection and evaluation

Analyze evidence instead of assuming neutrality

A dialogue, matching activity, cloze passage, labeled diagram, and reading passage introduce the main vocabulary and relationships. Multiple-choice and true-or-false questions ask students to reason about representation, unbalanced data, group outcomes, neutral code, and strategies for improving fairness. Short-answer prompts require students to explain why group comparisons matter and how better representation could be achieved.

The extended response asks students to explain how training data can create bias and how fair AI practices can address the problem using the terms bias, fairness, representation, and outcome. This brings the lesson concepts together in a focused written analysis.

Use it within an AI unit

A separate teacher guide PDF includes learning objectives, teaching tips, discussion questions, and common mistakes. Use this AI bias worksheet after introducing training data and machine learning, during a lesson on responsible artificial intelligence, or as practice analyzing how data choices affect model results. The resource gives students specific evidence to consider when discussing fairness rather than relying only on general opinions about AI.