The Aesthetics of Algorithmic Taste
Taste was once considered a deeply personal and social phenomenon—shaped by upbringing, education, and cultural exposure. Today, algorithms play a central role in curating what we see, hear, and consume. From Spotify playlists to Netflix recommendations, our aesthetic experiences are increasingly mediated by code. This article explores how algorithmic systems influence taste, redefine aesthetic judgment, and reshape cultural consumption.
1. Taste as Data
Algorithms interpret taste through:
- Behavioral signals (clicks, likes, watch time)
- Consumption patterns across platforms
- Social graph analysis and peer influence
- Feedback loops from engagement metrics
Taste becomes quantified, tracked, and predicted.
2. The Shift from Curation to Calculation
Traditionally, taste was shaped by:
- Critics, curators, and cultural institutions
- Peer communities and subcultures
- Personal exploration and serendipity
Now, algorithms act as infomediaries, replacing gatekeepers with predictive engines that optimize for engagement.
3. Personalization vs. Homogenization
Algorithmic taste offers:
- Hyper-personalized recommendations
- Discovery of niche content and creators
- Adaptive interfaces based on user behavior
But risks include:
- Echo chambers and aesthetic flattening
- Reduced exposure to unfamiliar or challenging works
- Reinforcement of dominant cultural norms
Taste becomes tailored—but potentially narrowed.
4. Aesthetic Judgment and Machine Mediation
Can algorithms make aesthetic judgments?
- They simulate preference, not perception
- They optimize for retention, not resonance
- They lack emotional or cultural depth—but mimic its effects
Judgment shifts from subjective contemplation to statistical inference.
5. Algorithmic Taste Formation
Platforms like Spotify, TikTok, and Netflix:
- Shape user identity through consumption
- Influence creative production via feedback loops
- Promote styles and formats that perform well algorithmically
Taste becomes a co-product of user behavior and platform logic.
6. The Politics of Recommendation
Recommendation systems reflect:
- Commercial incentives (maximize watch time, ad revenue)
- Cultural biases embedded in training data
- Opaque criteria for visibility and promotion
Taste is not neutral—it’s coded with values and priorities.
7. Creative Response and Resistance
Artists respond by:
- Designing for algorithmic visibility (hooks, thumbnails, pacing)
- Subverting recommendation logic with experimental formats
- Building communities outside mainstream platforms
Creativity becomes a negotiation with algorithmic aesthetics.
8. Expert Perspectives
Emanuele Arielli, philosopher of aesthetics:
“We are witnessing the externalization and automation of taste—where algorithms shape not just what we consume, but how we define beauty.”
Stan Erraught, cultural theorist:
“Taste used to be a sensus communis—a shared human judgment. Now it’s outsourced to systems that optimize for attention.”
Their views highlight the tension between human sensibility and machine mediation.
9. The Role of Transparency
To navigate algorithmic taste, we need:
- Clear disclosure of recommendation criteria
- User agency in tuning personalization
- Ethical design that balances novelty and diversity
Taste should be guided—not dictated—by algorithms.
10. The Road Ahead
Expect:
- AI-generated aesthetic profiles and taste maps
- Collaborative filtering across emotional and cultural dimensions
- Hybrid curation models combining human and machine judgment
- New aesthetic movements shaped by algorithmic logic
Taste will evolve—not disappear—but it will be co-authored by code and culture.
Conclusion
Algorithmic systems are not just curating our experiences—they’re shaping our aesthetic sensibilities. As taste becomes a function of data and prediction, we must ask: what does it mean to like something in an age where liking is optimized? The future of aesthetics lies in understanding how machines influence meaning—and how we reclaim agency in what we find beautiful.