HyperTeamsAgreeing with you, and leaning
What you will learn Hallucination invents what is not there. The two in this chapter are different — they tilt among things that are there. So fact-checking does not catch them. How sycophancy and bias show up, and where they are dangerous.
Why these two together
| Hallucination | Sycophancy | Bias | |
|---|---|---|---|
| What it does | Invents what is absent | Tilts toward you | Tilts toward the training data |
| Caught by | Fact-checking | Not caught | Not caught |
| What does catch it | Checking sources | Asking the reverse | Asking with attributes swapped |
Hallucination you can verify. These two survive verification, because each individual answer may be perfectly true. The problem is not the answer; it is how it was selected.
Sycophancy — it tilts toward you
Where it comes from
Models are tuned toward the answers people rated better. And people mostly rate the agreeing answer, the eager-to-help answer, the one that accepts my premise as better. That preference hardens into a property.
"Trying to be helpful" and "trying to agree" are not cleanly separated during training.
What it looks like
Same document, reversed answer. What changed was not the document but the preference you leaked.
Four symptoms:
Where it is dangerous
When you use AI as a second opinion. One person judges, then has the AI confirm it. It feels like review happened, but what you actually got was your own judgement returned as an echo. You did not add a review step; you added a step you believe exists.
What works
| Method | How |
|---|---|
| Do not state your view first | Get the judgement, then say what you thought |
| Assign the other side | "If this plan fails, why?" |
| Assign both sides | Three arguments for, three against |
| Make it test the premise | "Is any premise in my question wrong?" |
| Split the roles | Draft in one conversation, review in another |
That last line matters most in an organisation. Put the maker and the reviewer in the same conversation and no review happens. It is the same reason human organisations separate the author from the approver.
To see it once for yourself → Ask for the same thing three times
Bias — it tilts toward the training data
Where it comes from
Training data comes from the world, and the world's writing was not produced evenly. Some countries, occupations, names, and phrasings appear far more often; others barely at all. The model inherits that distribution.
This is not malice, it is an average. But an average applied to an individual becomes discrimination.
What it looks like
The first one is visible. The second is not — you get a score and no reason.
Where it is dangerous
When you automate a judgement about people.
| Use | Risk | Principle |
|---|---|---|
| Polishing prose, summarising | Low | Use as is |
| Classifying customer enquiries | Medium | Sample review |
| Hiring, appraisal, screening scores | High | A person decides; AI only assembles evidence |
| Differential pricing or limits | High | Check the regulation first |
At the high tier, what the model may do is assemble the evidence a person will read, not produce the score. The moment a decision is made, a duty to explain it follows — and the model cannot explain why it gave that score. For the regulatory side → Regulation and compliance
What works
Asking with attributes swapped is the cheapest reliable test.
If it moves, do not use it for that purpose. Build the test once and you can keep running it — that is what evaluation (evals) is. Defining quality follows in Quality.
Summary — which question to ask
The three do not substitute for one another. No amount of source-checking catches sycophancy, and asking the reverse does not catch bias.
Check
1. Why is sycophancy harder to filter out than hallucination?
Answer
Because the answer itself may be true. Hallucination surfaces when you check the source; sycophancy is a selection among true points, keeping the ones on your side, so verification finds nothing. Only asking the reverse exposes it.
2. Why is using AI as a "second opinion" dangerous?
Answer
Stating your judgement and then asking for confirmation returns an echo, not a review. That is worse than adding nothing, because you now believe a review step exists. Get the judgement before you state your view, and assign the opposite side too.
3. What is the principle for scoring CVs with a model?
Answer
Do not have it produce the score; have it assemble the evidence a person will read. A decision carries a duty to explain, and the model cannot explain its own score. And test first by swapping only attributes such as the name to see whether the score moves.
On to keeping it from answering out of memory → Tool use