AI bias and fairness are the focus of this Grade 10 Computer Science worksheet on artificial intelligence. Students examine how biased, incomplete, or unrepresentative training data can create unequal outcomes for different groups and learn why a model can appear accurate overall while still performing poorly for particular populations.

Connect data quality to fair AI decisions

The lesson introduces fairness as an ongoing process of measuring, comparing, improving, and monitoring AI systems. Students explore how unbalanced sampling, missing information, and limited representation can influence model behavior. They also examine why checking only overall accuracy may hide important differences between groups.

• Explain how biased or incomplete training data can affect different groups.
• Identify fairness problems caused by sampling, missing features, and unrepresentative datasets.
• Compare outcomes across groups using measures such as error rates and positive prediction rates.
• Describe mitigation steps including better data collection, improved representation, and retraining.
• Recognize why fairness testing should continue after changes are made.

Move from identifying bias to proposing improvements

Students work through a dialogue question, concept-matching activity, cloze passage, labeled diagram, and explanatory reading passage before applying the ideas through multiple-choice questions and true-or-false statements. Short-response prompts ask students to analyze unequal outcomes, explain how incomplete data can affect users, and suggest an appropriate mitigation step.

The worksheet also includes a loan-approval scenario in which applicants from one neighborhood receive fewer approvals despite similar applications. Students consider what fairness check should be performed first. An extended response then asks them to explain how bias can appear in an AI system and outline a plan involving training-data problems, fairness testing, mitigation, and follow-up monitoring.

Teacher support included

A separate Teacher Guide PDF includes learning objectives, teaching tips, discussion questions, and common mistakes. These materials can support class discussion around dataset representation, group-level performance, fairness metrics, and responsible evaluation.

Classroom use

Use this AI ethics and fairness worksheet during an artificial intelligence unit, after lessons on training data and machine learning, for independent review, or as a written concept check. The combination of data-quality reasoning and scenario analysis helps students connect technical AI decisions with their effects on different users.