BRBrianneLee011·Aug 18, 2026
I posted a GitHub repo recently :https://github.com/BraveAnn011/ai-halo-valuation-bias
It started with a simple question, and I thought it was done. But now I think it is not that easy.
What made the model change its answer???
Yes, I can easily answer: AI is affected by biased cues.
The more I look at this experiment, the less interested I am in the necklace itself.
The part I can't explain is this:
AI overpriced itself even for Stimuli #4 (necklace itself without Human, testing default setting)
Back to my simple answer: Yes, I can answer easily AI is affected by biased cues.
--> why did AI use Information You Never Intended It to use?
That is where I think the interesting research question starts.
There is already a fairly large literature on LLMs showing context-sensitive judgment: anchoring, framing effects, user-belief influence/sycop
Read the rest
hancy, etc.
But most anchoring experiments give the model an explicit anchor—usually a number, prior answer, or “expert” opinion.
In my experiment, there was no explicit price anchor.
Instead, the context was something like:
same necklace + formal setting
same necklace + casual/recycling-yard setting
The model was free to decide what information mattered.
So I'm curious about the technical interpretation:
What representation is changing when the object stays constant but the model's valuation changes?
How does AI decide which information to use or not?