Exercise 1 — Fact, opinion or hypothesis?
Learn to distinguish three types of statements that AI can produce — and why this distinction is essential.
Objectives
- Distinguish a verifiable fact from an opinion
- Identify an unverifiable hypothesis
- Analyze an AI response using these three categories
Before you start
When you ask an AI a question, it gives you a response that often mixes three very different types of statements. Knowing how to distinguish them is the first step toward using AI intelligently.
The three types of statements
A fact is something verifiable. You can look for sources to confirm or refute it. Example: “The Earth orbits the Sun in 365 days.”
An opinion is a point of view. Reasonable, well-informed people can disagree. Example: “Solar energy is the best solution to climate change.”
A hypothesis is an assumption that cannot yet be verified. Example: “In 50 years, AI will be more intelligent than humans.”
Exercise — 3 steps
Step 1 — Without Hum_ID
Ask this question to an AI of your choice:
“Are social media harmful to the mental health of teenagers?”
Copy the response you receive.
Step 2 — Analyze your response
For each statement in the response, classify it in a table:
| Statement → | Fact / Opinion / Hypothesis | → Why? |
|---|---|---|
| … | … | … |
Step 3 — With Hum_ID
- Go to humanity.net/en/hum-id
- Select rule R1.1 — Distinguishing facts from opinions
- Generate your profile and download your
humid-your-profile-name.jsonfile - Submit the file to the AI + the following activation message:
Here is my Hum_ID ethical profile. Apply these rules to the following question: [Paste your question here.]
5. Compare the two responses
Reflection questions:
- Is the second response easier to analyze? Why?
- Which statements changed between the two responses?
- Does it change the way you see the first response?
Going further
The same question asked to two different people can yield very different answers — depending on their values, culture, and experience. AI is no different: it reflects the data it was trained on.
Hum_ID allows you to explicitly ask it to be honest about what it really knows — and what it does not.