AI Bias Analysis

In this worksheet, students analyze two “data recipes” that demonstrate how bias can appear in artificial intelligence systems. The activity invites students to read each scenario closely, identify which “ingredients” (types of data) may be causing unfair outcomes, and propose substitutions that would make the AI system more equitable. Learners must think critically about how training data, missing information, and flawed assumptions can shape real-world AI decisions. The tasks also prompt reflection on why biased data leads to biased predictions and how these issues can be fixed. Through these questions, students learn to evaluate technology with a critical lens and understand the connection between data quality and fairness in automated decisions. This strengthens higher-order reasoning, analytical reading, and digital literacy skills.

Curriculum Matched Skills

English Language Arts – Critical Reading and Analysis of Informational Texts

English Language Arts – Citing Evidence and Explaining Reasoning

Social Studies – Digital Citizenship and Ethical Use of Technology

Technology Education – Understanding Data Bias and Algorithmic Decision-Making

Social Studies – Evaluating Sources and Recognizing Bias

This worksheet is part of our Role of Bias in AI collection.