Blogs / AI and Public Taste: How Technology Shapes Our Preferences

AI and Public Taste: How Technology Shapes Our Preferences

هوش مصنوعی و سلیقه عمومی: چگونه تکنولوژی ترجیحات ما را می‌سازد

Introduction

In today's digital world, every morning when you wake up and pick up your smartphone, the first thing you see is the product of artificial intelligence and its complex algorithms. The video YouTube recommends to you, the song Spotify plays for you, the movie Netflix shows you, and even the news you read on Twitter - all of them are selected by machine learning algorithms. These AI systems don't just predict what you like; they gradually shape your taste.
But have you ever wondered how artificial intelligence has become powerful enough to influence the taste of millions of people around the world? Why do we all suddenly fall in love with a particular song? Why does a movie suddenly go viral? And why do you sometimes feel that everything on the internet is "similar"?
In this in-depth article, we'll explore how AI algorithms use big data analysis and natural language processing to build, change, and sometimes even manipulate public taste. From the architecture of transformer models behind these systems to their psychological and social impacts, we'll examine everything.

How AI Works: Behind the Scenes

Recommendation Systems: The Brain Behind Our Choices

The core of public taste formation in the digital world is AI-Powered Recommendation Systems. These systems analyze behavioral patterns of millions of users using supervised learning and unsupervised learning.
Let's start with a tangible example: YouTube's algorithm. Every time you click on a video, the time you watch it, the videos you skip, the comments you leave, and even the speed at which you scroll the page - all this information is recorded and analyzed by the algorithm.
This data enters a recurrent neural network that, using deep learning techniques, creates a precise profile of you. This profile not only knows your current preferences but can also predict what you will like in the future.

Technical Architecture: From Raw Data to Manufactured Taste

Let's go deeper and see how these systems work at a technical level:
1. Data Collection: Platforms collect billions of data points - from watch time to geographic location, time of day, device type, and even how you move your mouse or finger.
2. Preprocessing: This data is cleaned, normalized, and categorized using data mining techniques.
3. Feature Extraction: Algorithms identify key features. For example, a video might receive labels like "entertaining," "educational," "short," "fast-paced," "emotional."
4. Modeling: Using convolutional neural networks for image analysis, LSTM networks for time series analysis, and attention mechanisms to focus on important parts, a complex model is built.
5. Ranking and Recommendation: Finally, the algorithm ranks available content and shows you what has the highest probability of engagement.
Platform Main Algorithm Type Main Ranking Criterion Impact on Taste
YouTube Deep Neural Networks + Reinforcement Learning Watch Time Very High - Strong Filter Bubble
TikTok Collaborative Filtering + Graph Neural Networks Video Completion Rate & Engagement Extreme - Rapid Trend Changes
Spotify Clustering Algorithms + Matrix Factorization Complete Playback & Playlist Addition Moderate - Diversity in Recommendations
Netflix Ensemble Learning + Contextual Bandits Watch Completion & Rating High - Taste Homogenization
Instagram Multimodal Models + Machine Vision Dwell Time & Interactions Very High - Creating Visual Trends

Psychological Mechanisms: Why Do We Listen to AI?

Feedback Loop and Digital Addiction

AI algorithms are not just technical tools - they leverage deep knowledge of behavioral psychology. Every time you like recommended content and interact with it, your brain releases dopamine. This is the same substance involved in the body's natural reward processes.
AI algorithms recognize and exploit this feedback loop. They choose the next content to maximize the likelihood of more dopamine release - but not always. This technique, called "Variable Reinforcement" in psychology, is the same thing slot machines use to addict people.
Imagine you're scrolling through Instagram. Sometimes you see a super interesting post, sometimes a normal post, and sometimes something you don't really like. This very uncertainty makes it impossible to stop scrolling - because you don't know how interesting the next post will be.

Filter Bubbles and Echo Chambers

One of the most controversial effects of algorithms is creating "Filter Bubbles". Algorithms gradually place you in a world that only shows content aligned with your current beliefs and preferences.
Real example: Suppose you once clicked on a political video with a specific orientation. YouTube's algorithm starts showing similar videos. The more you watch, the more confident the algorithm becomes that this is what you want. Gradually, you no longer see any opposing viewpoints - and this is an "Echo Chamber".
This phenomenon has profound effects on social media and even democratic processes. Societies become polarized groups, each living in their own bubble with constantly reinforced beliefs. For a deeper understanding of this phenomenon and learning practical strategies to escape filter bubbles, read our article "Filter Bubbles and Echo Chambers: How to Escape the Algorithm Trap".

Tangible Examples: How Your Taste Was Built

Music: How a Song Goes Viral

Have you ever wondered why suddenly everyone is listening to a particular song? Let's see behind the scenes of this process.
On Spotify, the algorithm can detect that a song is becoming popular using time series forecasting - even before it actually goes viral. This algorithm considers many factors:
  • Listener Growth Rate: Is the number of people listening to this song rapidly increasing?
  • Save Rate: How many people are adding this song to their playlists?
  • Repeat Playback: Are people listening to it again and again?
  • Social Sharing: Is it being spread on social networks?
  • Audio Features: Is the melody, rhythm, or song structure similar to previous viral songs?
When the algorithm detects a song has viral potential, it starts showing it to more users. This is somewhat a self-fulfilling prophecy - the algorithm makes the song popular because it thinks it will be popular!
TikTok has taken this process to a new level. On this platform, a song can go from obscurity to millions of plays within hours. TikTok's algorithm, using diffusion models and swarm intelligence techniques, can quickly identify and amplify emerging trends.

Fashion and Clothing: How a Style Becomes Global

The fashion industry is one of the industries profoundly transformed by algorithms. Platforms like Pinterest and Instagram, using vision transformer models, can analyze and categorize clothing styles.
Suppose a new fashion brand produces a jacket with a specific pattern. Instagram, by analyzing images and using AI image processing, can detect that this pattern is becoming popular. Then it starts showing posts related to this style to potentially interested users.
But here's what's more interesting: the algorithm can also suggest new combinations. For example, if it sees that people who like that jacket usually also wear specific jeans, it suggests this combination to others. Thus, the algorithm not only follows taste but creates it.
This is what happens in the fashion industry with artificial intelligence - a self-feeding cycle where algorithms both recognize and amplify trends.

Film and Series: Netflix and Algorithm-Driven Content Production

Netflix is one of the pioneering companies in using data to shape content. This company not only uses algorithms to recommend content but also leverages them for content production.
When Netflix decided to produce the series "House of Cards," this decision was completely data-driven. Netflix's algorithms had shown that:
  • Films directed by David Fincher are popular
  • Films and series starring Kevin Spacey have many viewers
  • The original British "House of Cards" has many fans
  • These three audience groups have significant overlap
By combining this data, Netflix knew that a political series with this combination would be successful - and it was. But the interesting point is that this success was not only because of the series' quality but because Netflix's algorithm precisely showed it to the right people.
Today, Netflix, using predictive models, can even detect which scenes in a film are more liked. This information is given to content creators to build future content based on it.

Social and Cultural Impacts

Cultural Homogenization or Diversity?

One of the hot debates is whether algorithms cause cultural homogenization or increase diversity. The answer is complex.
On one hand, algorithms can introduce you to diverse content you would never find yourself. For example, Spotify might introduce you to a lesser-known artist from another country that you fall in love with.
But on the other hand, the same algorithms make us all see similar content. When a trend starts, algorithms amplify it exponentially. This causes "medium-grade" content - those that are neither very popular nor very unpopular - to be seen less.
This phenomenon leads to a "Winner-Takes-All Economy". A few top artists receive billions of streams, while thousands of other artists barely have a few hundred listeners.

Language and Communication Changes

Algorithms even affect our language. Words, phrases, and even tones that go viral on social networks quickly enter daily speech.
TikTok is particularly powerful in this area. A video with a specific phrase might be reshared millions of times, and within days, everyone is using that phrase. TikTok's natural language processing algorithm identifies and amplifies these patterns.
These impacts even affect our social skills. Today's youth communicate more through memes and short videos than long conversations.

The Dark Side: Manipulation and Exploitation

Amplifying Extreme Content

One of the most concerning effects of algorithms is their tendency to amplify extreme content. Why? Because extreme content usually has more engagement - people click on it more, comment more, and share it more.
A study showed that YouTube's algorithm tends to guide users toward more extreme content. If you watch a moderate political video, the algorithm might suggest videos with more extreme views - because data shows people watch them more.
This has raised serious ethical concerns about platform responsibility.

Political Use of Algorithms

In recent elections worldwide, using algorithms for political targeting has become a controversial issue. Political campaigns can show specific messages to specific groups by analyzing behavioral data.
For example, if the algorithm detects you're concerned about the economy, political ads about the economy are shown to you. If you're concerned about security, you see messages about security. This "microtargeting" can be very effective - and very dangerous.

Addiction and Mental Health

Algorithms are designed to maximize engagement - not maximize your wellbeing. This is an important difference. They want to keep you on the platform, even if it's detrimental to your mental health.
Research has shown that excessive use of algorithm-driven social networks can lead to depression, anxiety, and feelings of loneliness. Social comparison plays a big role here - when the algorithm only shows you the best moments of others' lives, you feel your life is lacking.
The issue of the danger line of chatbot usage and algorithm-driven platforms should also be taken seriously.

The Future: Where Are We Going?

AI and Hyper-Personalization

The future of AI and its algorithms is moving toward hyper-personalization. With advances in foundation models and large language models, future AI systems will not only know your preferences but can live adjust content for you.
Imagine a video you're watching changes based on your moment-by-moment reactions. If you get distracted in one part, the video automatically moves to the next interesting part. This type of technology is being developed using emotional AI and multimodal models.

The Rise of the Metaverse and Virtual Taste

With the growth of the metaverse, algorithmic taste-making will also expand into the virtual world. Algorithms will not only determine what content you see but what experience you have, who you meet, and even how you appear.
Digital avatars and smart personal avatar design will also be influenced by algorithms.

Ethical and Transparent AI

Fortunately, there's a movement toward building more ethical AI systems. Explainable AI allows users to understand why specific content is shown to them.
Some platforms are testing mandatory diversity in recommendations - deliberately showing content different from your usual pattern to prevent filter bubbles.
There are also discussions about AI trustworthiness and the need for industry standards.

How to Take Back Control?

Individual Strategies

Although algorithms are powerful, you can still have control over your taste. Here are some practical strategies:
1. Algorithm Awareness: The first step is knowing the algorithm is at work. Every time content is recommended to you, ask yourself: "Do I really want this, or does the algorithm think I want it?"
2. Active Search: Instead of relying completely on recommendations, actively search for new content. On Spotify, instead of just listening to Discover Weekly, search for new genres.
3. Clearing History: Sometimes, clearing watch or listening history can "reset" the algorithm and give you more diverse recommendations.
4. Different Interaction: Deliberately interact with content you don't usually see. This signals to the algorithm that your interests are more diverse than it thinks.
5. Using Alternative Platforms: Sometimes the best way is to use smaller, non-algorithmic platforms that offer different content.
For those who want a deeper understanding of these technologies, learning programming and building applications with artificial intelligence can be helpful.

Platform Responsibility

But the pressure shouldn't only be on users' shoulders. Platforms must also take responsibility:
  • Transparency: Clear explanation of how the algorithm works
  • User Control: More tools for users to adjust the algorithm
  • Mandatory Diversity: Ensuring users see diverse content
  • Protection of Vulnerable Groups: Especially children and teenagers who need AI education
  • Independent Auditing: Allowing independent researchers to examine algorithms

The Role of Legislation and Policy

At a macro level, there's a need for laws and regulations. The European Union, with its "Digital Services Act," is a pioneer in this area. This law requires platforms to be more transparent about their algorithms.
Similar discussions about AI's role in government and judicial systems are also ongoing.

Conclusion: Taste in the AI Age

Artificial intelligence and its algorithms are unavoidably part of our digital lives. These systems have amazing power - they can introduce us to incredible content we would never find ourselves, they can give lesser-known artists opportunities to reach new audiences, and they can dramatically improve user experience.
But as we've seen, they can also trap us in filter bubbles, fuel extreme content, and even threaten our mental health. The impact of artificial intelligence on public taste is deep and multifaceted - from the music we listen to to the clothes we wear, from our political views to the language we speak.
The answer is neither complete rejection of technology nor blind acceptance. The answer lies in awareness, criticism, and active engagement. We must learn to coexist with AI - benefit from its advantages but not lose control.
Ultimately, your taste should be your taste - not the product of an algorithm. But in a world where AI is becoming increasingly powerful, maintaining this independence requires constant effort and awareness. By understanding how these systems work, we can make more informed decisions - both as individuals and as a society.
The future of artificial intelligence and algorithms is bright, but its shape depends on our choices - as users, developers, legislators, and citizens. Let's build this future with awareness and responsibility.