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Your Taste Was Never Entirely Yours

Algorithms did not invent influence, but they have made the environment shaping our preferences more personalized and invisible.

by Bruno Correa9/29/2026Updated 9/29/20262 min read
✓ Reviewed by HumaneviInterpretation
Your Taste Was Never Entirely Yours

We like to think of taste as something that comes from deep inside us.

The music we love, the books we choose, the clothes we wear, the films we recommend and even the ideas that attract us can feel like evidence of a private identity.

But taste has never been created in isolation.

Preference begins with exposure

Family introduces us to certain foods and music. Friends recommend artists. Schools shape literary references. Cities expose us to styles. Generations develop their own symbols.

We can only choose among things we have encountered.

Before we can prefer something, it usually has to become visible to us.

Algorithms influence the menu before we choose

Recommendation systems do not merely observe preference. They participate in the environment from which preference develops.

A song appears repeatedly. A type of video receives more space. Certain books are recommended while others never enter the screen. Over time, repetition becomes familiarity and familiarity can become liking.

This does not mean platforms fully control us. Human preference is more complex than that. But it does mean the boundary between “I chose this” and “this was repeatedly placed in front of me” is sometimes difficult to see.

Personalization can narrow surprise

Good recommendations are useful because they reduce search costs. But a system that becomes too good at predicting us may keep returning us to versions of what we already know.

Discovery becomes efficient and, paradoxically, less surprising.

Curiosity sometimes needs friction

There is value in occasionally stepping outside prediction: entering a bookstore without a list, listening to an unfamiliar genre, reading a publication you normally ignore, asking a friend with different tastes for a recommendation.

These small acts create encounters that an optimized feed might never choose.

Autonomy does not require rejecting recommendations. It may require preserving some spaces where recommendation does not decide first.

Perhaps discovering who we are still depends partly on finding things that no system expected us to like.

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