LLM and Transformer Foundations for Practical AI Users
Lesson 4: Bias, Limits, and Responsible Task Fit
Lesson Objectives
By the end of this lesson, students should be able to:
- Explain why training data can carry bias.
- Recognize when AI output needs fairness or safety review.
- Build a task-fit checklist before using AI output.
Lesson Content
Language models learn from data created by people. That means they can learn useful language patterns and harmful patterns at the same time. Bias can show up in job suggestions, descriptions of groups, safety assumptions, translations, image labels, or summaries.
Fine-tuning or better prompting can reduce some problems, but students should not assume bias disappears. Responsible use means matching the AI task to an appropriate risk level and adding review when output affects people.
For everyday users, the rule is practical: if the answer affects someone's money, access, education, reputation, health, safety, or rights, slow down and review it more carefully.
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