Explore bias and fairness in artificial intelligence with a Grade 9 Computer Science worksheet that challenges students to examine how training data influences AI predictions and decisions. Students investigate why incomplete, unbalanced, outdated, or unfairly collected data can lead to biased results, how representation affects system performance, and why fairness requires ongoing evaluation rather than simply collecting more examples.
Grounded in practical scenarios involving image recognition and school learning-resource recommendations, this resource connects essential AI ethics concepts to situations students can analyse and explain. A separate teacher guide provides instructional support for introducing the topic, facilitating discussion, and addressing common misconceptions.
From training data to real-world consequences
Students explore the relationship between the examples an AI system learns from and the decisions it makes. The reading passage introduces the distinctions between bias, representation, fairness, and outcome, using concrete examples to show how weaknesses in training data can disadvantage particular groups.
Students consider why an image-recognition system trained mainly on photographs taken in bright sunlight may struggle in dim conditions, and how a school recommendation system may overlook students whose reading levels or access to technology differ from those represented in its data. These examples establish the foundation for discussing responsible AI development and the limitations of automated decisions.
What students practise
- Dialogue and summary: Engage with a dialogue-based activity that introduces the topic.
- Matching and cloze passage: Connect key concepts with their meanings and complete a paragraph using relevant AI vocabulary.
- Diagram labelling: Work with a labelled-diagram activity related to the lesson.
- Reading comprehension: Extract information about training data, representation, unequal error rates, human review, and ongoing monitoring.
- Multiple-choice questions: Apply reading details and conceptual knowledge to practical AI scenarios.
- True-or-false statements: Evaluate claims about dataset quality, fairness testing, bias, and monitoring.
- Short-answer questions: Explain methods for investigating bias, analyse a plant disease detector with limited training examples, and distinguish bias from an outcome.
- Extended response: Develop a reasoned plan for improving a school recommendation system through better data representation, fairness testing, monitoring, and human review.
Practical strategies, not just definitions
The worksheet moves beyond recognising terminology. Students examine how developers can compare error rates across groups, test systems with varied examples, collect accurate and relevant data responsibly, seek feedback from affected people, and provide human review for important decisions. They also consider why adding a large quantity of unchecked data does not guarantee reliable or fair results.
This makes the resource useful for teaching AI ethics, responsible technology use, and critical thinking about automated decision-making. Students must support their reasoning with examples and explain how technical choices can influence people's experiences.
What is included
- Printable student worksheet: Activities, a reading passage, comprehension checks, short written responses, and an extended-response task.
- Separate teacher guide PDF: Learning objectives, teaching tips, discussion questions, and common mistakes.
The teacher guide is a distinct planning resource, while the worksheet provides the student-facing learning and assessment activities. Together, they support lesson preparation and classroom discussion without requiring teachers to create all the prompts themselves.
Ways to use this Grade 9 resource
Use the worksheet during an introductory artificial intelligence unit, as guided reading and independent practice, or as a review of AI bias and fairness. The dialogue and reading can introduce key ideas before students complete the vocabulary activities and comprehension questions. Short answers and the extended response can then support written assessment or a class discussion about responsible AI design.
Curriculum information: The resource is identified under a general framework, with no specific curriculum code supplied. It is suited to Grade 9 Computer Science instruction focused on artificial intelligence, training data, representation, and fairness.
