this post was submitted on 09 Aug 2023
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Privacy
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The companies that aggregate data and find patterns in them can probably predict a lot about you from your music listening habits, when they correlate it with data about other people, or even about yourself. The power of profiling isn't in any specific data but in the patterns that emerge when you gather a lot of diverse data about a lot of people.
Listening habits will tell them about your routine, including where you are, when, and when you have time to listen to music (so, therefore, when you don't). If you don't ever listen to music between 8pm and 10pm, for example, it may indicate that you have children to put to bed. If you listen mostly between 12am and 5am it may indicate that you work a nightshift. If you listen between 8 and 9 and again between 5 and 6, you're probably a commuter. When you listen on a computer and when on mobile will tell them something too. And these are only the obvious patterns that I can think of off the top of my head. AI systems running on big data are designed to find patterns humans don't notice.
And of course the styles of music you listen to will be readily correlated with demographic profiles. When you feed data into AI systems designed to find patterns people can't spot, you'll find the most unlikely data reveals things about people that they'd never imagine you could know.
Given this, it's entirely possible that your music listening telemetry could eventually influence your credit score, your insurance premiums, your qualification for security clearances or your employability. You don't know where the data ends up, or with what other data it's correlated. This is why it's desirable in general to keep data private if it's not needed to provide the service.