AI BASICS
Assessing AI-Generated Content – Don’t Just Trust, Verify!
By the end of this unit, you will understand why critically evaluating and validating information generated by GenAI tools is needed, identify common issues with AI outputs (like hallucinations or biases), and learn practical strategies to verify the reliability and appropriateness of GenAI content for academic and professional use.
Key Takeaways
- AI is a starting point, NOT the final answer.
- Always assume AI outputs might be flawed until verified.
- Use multiple validation strategies: critical thinking, cross-referencing with reliable sources, and checking specific details.
- You are responsible for the accuracy and integrity of any information you use, regardless of its origin.
Common Issues: What to Watch Out for in AI Outputs
Factual Inaccuracies (“Hallucinations”)
What it looks like: The AI confidently states something as a fact, but it’s incorrect, made-up, or misattributed. It might invent statistics, historical events, or even fake citations and sources.
Why it happens: The AI is designed to produce plausible-sounding text, even if it doesn’t have the correct information.
Bias
What it looks like: The output may reflect societal biases present in its training data related to gender, race, age, culture, or other characteristics. It might use stereotypes, present one-sided viewpoints as neutral, or omit important perspectives.
Why it happens: The AI learns from human-generated text, which itself contains biases.
Outdated Information
What it looks like: The information provided might be correct for a certain point in time, but is no longer current. This is especially true for rapidly evolving fields or recent events, as many models have a “knowledge cut-off” date.
Why it happens: The AI’s training data isn’t updated in real-time.
Lack of Nuance or Oversimplification
What it looks like: Complex topics may be presented too simply, missing critical details, counterarguments, or the subtleties that are essential for academic understanding.
Why it happens: The AI aims for common patterns, which can smooth over complexities.
Generic or Superficial Content
What it looks like: The output might be grammatically correct and sound reasonable but lacks depth, originality, or specific insights relevant to your unique prompt or academic needs.
Why it happens: Without very specific prompting, the AI might default to common or general information.
Source Unreliability (Even if Cited)
What it looks like: The AI might provide sources or citations that look real but are fabricated, refer to non-existent articles, or misrepresent the content of actual sources.
Why it happens: It is good at mimicking the format of citations, not always at verifying their legitimacy.
Strategies for Validating AI Outputs
Think of yourself as an editor and fact-checker for the AI.
