Yes, but you can make assumptions based on what you know about humans generally. Like their example that if you ask if you have long hair. If you answer yes the likelihood is you are probably female.

You can think of all sorts of questions and answers like this, and when you combine with the assumptions and answers from previous answers you can make even more assumptions. They won't always be correct, but you don't have to be "perfect", depending on your use-case. For example for advertising purposes assumptions(even if incorrect) can still go a long way.

There is a reason Target got sooo good at identifying pregnant women[0] before the women knew they were pregnant that they creeped out women, and had to pull back what they did with that information. This was like a decade or more ago. It's only gotten more accurate since then.

0: one example from 2012: https://techland.time.com/2012/02/17/how-target-knew-a-high-...

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armchairhacker 2 hours ago | parent | next [-]

https://medium.com/@colin.fraser/target-didnt-figure-out-a-t...

https://www.predictiveanalyticsworld.com/machinelearningtime...

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codedokode 3 hours ago | parent | prev [-]

> Target got sooo good at identifying pregnant women

That's why I pay with cash and do not have a loyalty card (other customers often offer theirs at cash register anyway). And of course I don't even go to Target.

	▲	georgefrowny 2 hours ago \| parent [-]
		I don't know if Target specifically use all of these, but I would bet they have data based on at least some of facial/gait/demographic recognition, wi-fi/Bluetooth beaconing, vehicle registrations, time and location tracking, statistical analysis of your purchases and clustering of people you have made purchases next to (e.g. you bought something at same time and till as your mother more then once). I'm sure they have other methods too. They can also combine datasets from brokers that do have a face:name link (say you used a card at another store that captured it and sold the data) and resolve you within their own data that way.