A profile can say you are interested in something you never told anybody you liked.
That is not necessarily an error.
It may be the product working as designed.
Advertising systems routinely infer interests, habits, or purchase intent from behavior and then place users into audience segments.
The important word is infer.
Behavior becomes a label
Google’s current advertising documentation says Demand Gen audiences can include groups based on interests, habits, active research, demographic information, or prior interaction with a business. Google describes these audience categories as estimates and says its systems may classify people into groups such as sports fans, travelers, or people currently shopping for cars. See Google Ads’ Demand Gen audiences overview and About audience segments.
The label may therefore come from observed behavior rather than a form where the user checked:
I am currently shopping for a car.
A system might infer that interest from searches, videos, app activity, website visits, purchases, or other signals available to the platform.
The Federal Trade Commission’s 2024 report on large social-media and video-streaming services found that companies maintained user-interest information and used those interests primarily for targeted advertising. The report noted examples such as food, nightlife, parental-status-like categories, and shopping-interest segments. See A Look Behind the Screens.
An inference is useful precisely because it goes beyond volunteered data
If advertisers could target only facts people explicitly entered into profile forms, many useful commercial categories would be missing.
Someone researching tents, hiking boots, trail maps, and national parks may never click a button labeled Outdoor enthusiast.
A model can still make the inference.
That can make advertising more relevant.
It can also create labels the person never sees and never had an opportunity to correct.
The label can be wrong or stale
Behavior is ambiguous.
You may research diabetes for a relative.
You may shop for baby products for a coworker’s shower.
You may read luxury-car reviews because the engineering is interesting while having absolutely no intention of buying one.
You may spend a week researching divorce law for an article.
The resulting segment can mistake curiosity, work, gifts, research, or one-time events for stable personal interest.
And even a correct inference can expire.
A person who was shopping for a refrigerator last month probably does not want to be classified as a refrigerator enthusiast until retirement.
Segments change what the system decides to show
Once assigned, audience labels can affect ad eligibility, bidding, recommendations, campaign optimization, measurement, and other automated decisions.
That does not mean every segment produces an important consequence.
It means the system has turned behavior into a proposition about the person.
The Surveillance Economy does not only collect facts.
It manufactures new data from old data.
A browser history is one dataset.
What we think this person wants is another.