AI bias worksheets for Grade 6 Computer Science help students explore how artificial intelligence systems can produce unfair or unreliable results when their training data is incomplete or unbalanced. Through examples involving picture recognition, recommendation systems, and application sorting, students investigate why bias occurs, how it affects AI outputs, and what developers can do to improve fairness.

Rather than treating artificial intelligence as a system that always makes correct decisions, this resource encourages students to examine the relationship between training examples and system results. Students consider how missing information, limited representation, and patterns from past unfair decisions can influence AI behavior, even when developers do not intend to create unfair outcomes.

What Students Explore

• Training data and bias: Explain how incomplete or unrepresentative examples can cause an AI system to perform poorly in situations it rarely encounters.
• Fairness in AI: Compare outcomes from balanced and unbalanced training data and identify possible patterns of unfairness.
• Real-world applications: Examine bias in dog picture recognition, activity recommendations, application sorting, and plant recognition.
• Testing and improvement: Describe how representative data, varied test examples, error investigation, human review, and regular evaluations can help improve fairness and reliability.
• Critical evaluation: Explain why adding examples alone does not guarantee fairness and why AI results should be examined carefully.

Activities Included in the Worksheet

Students work through a connected sequence of activities that moves from key concepts to reading comprehension, analysis, and extended reasoning. A dialogue and summary activity introduces ideas for discussion, while matching and a cloze passage reinforce important vocabulary, including data, bias, fairness, result, errors, balanced, and limitations. A labeled diagram activity provides an additional way to explore how bias relates to AI systems.

The reading passage, Artificial Intelligence and Bias, explains how training data influences system performance and why developers must evaluate results across varied examples. Multiple-choice questions and true-or-false statements assess students' understanding of the passage. Short-answer questions ask students to apply the concepts to a library book recommendation tool, while the extended-response task challenges them to analyze a plant recognition system with limited examples of native wild plants and recommend practical improvements.

What's Included

• ✔️ Grade 6 Computer Science worksheet on artificial intelligence and bias
• ✔️ Reading passage with comprehension and application questions
• ✔️ Dialogue and summary, matching, cloze passage, and labeled diagram activities
• ✔️ Multiple-choice, true-or-false, short-answer, and extended-response questions
• ✔️ Separate PDF teacher guide with learning objectives, teaching tips, discussion questions, and common mistakes

Classroom Use

Use this AI ethics worksheet during an introductory artificial intelligence lesson, a Computer Science unit on machine learning, or a discussion of responsible technology. Students can complete the activities independently, work through the reading and questions in small groups, or use the extended-response prompt to explain their reasoning in writing. The separate teacher guide provides practical support for preparing the lesson, leading discussion, and addressing common misconceptions about AI fairness.

The resource uses the general curriculum framework rather than claiming alignment to a specific named standard. It gives teachers a focused way to introduce algorithmic bias, training data quality, and responsible AI evaluation while helping students build evidence-based explanations of how automated systems can affect decisions.