AI Art Tools Are Flattening Cultural Meaning Into Algorithm-Friendly Data
A new study reveals how text-to-image generators like DALL-E and Stable Diffusion strip away the historical and cultural context embedded in artistic styles, treating nuanced human concepts as simple data points. For companies deploying these tools commercially and policymakers regulating AI, the finding exposes a hidden bias: Western art history categories are being baked into systems that claim to work universally.
Originaltitel: The Reification of Style in AI Image Generation
<p>Over the past decade, an active subfield in computer vision and artificial intelligence research has sought to identify and assign style in image data. Collections of digitized artworks have been central to much of this research. More recently, text-to-image generation tools, such as DALL·E, Midjourney, and Stable Diffusion, have made the replications of artistic style central to their operation. In art history, style is a highly contested category and the terminology used is understood to be historically situated. In other words, style terms are dependent on context, reflecting the differing purposes of art historians, artists, or critics at different points in history. When style terms become data and enter into automated processes through machine learning, these nuances are often lost. This paper addresses how contemporary artificial intelligence methods reify the Western concept of style and what that means for the study of visual culture.</p>