Can AI Understand Beauty? The Limits of Machine Perception and the Role of Human Judgment

AI can learn patterns of beauty based on data. For example, if AI studies thousands of pictures and paintings, it can statistically identify which images people consider beautiful. However, this is not true understanding. Human aesthetic judgment is a complex experience involving emotions, culture, context, and philosophical reflection. Since AI lacks emotions, it cannot comprehend why something is perceived as beautiful.

AI can analyze aesthetic elements in images, music, and writing—such as proportion, harmony, color, melody, and structure—and produce works that resemble existing beauty. It can recommend or generate artworks tailored to people’s preferences by identifying common aesthetic trends. However, beauty varies across time, culture, and individuals, and depends on historical context and the artist’s intention. AI cannot grasp the emotions that accompany aesthetic experiences, such as admiration, awe, or being deeply moved.

AI may appear to imitate and judge beauty, but it is difficult to say that it truly understands it. Nevertheless, it can be a valuable tool to assist human aesthetic experiences or foster new forms of creativity. As researcher Josef Doctorovitz (2024) noted, even if AI appears to create beauty, it is not feeling or judging like a human, it is merely calculating based on data. The researchers used validated questionnaires to measure self‑esteem, body‑image satisfaction, and the extent to which students compare themselves with others. They found that students who frequently viewed AI‑generated “ideal” images were more likely to have lower self‑esteem and to feel dissatisfied with their own bodies. In other words, repeatedly comparing themselves with the seemingly flawless AI images on the screen led them to view themselves more negatively. (Rashida Tufail, 2024) or instance, images of “beautiful women” generated by AI often depict youth, fair skin, sharp facial features, and sexually idealized appearances. AI tends to reproduce beauty standards long shaped by fashion, advertising, and media. This distortion is further reinforced by billions of anonymous images—often including pornography—used in AI training. As a result, visual stereotypes are spreading more powerfully than ever.

AI follows patterns of beauty most frequently selected from the data. Consequently, a specific race, body type, facial structure, and style are repeatedly reproduced as a kind of “standard,” reinforcing socially harmful beauty norms. On social media especially, “AI-generated ideal type” images may trigger comparisons and feelings of inferiority, negatively affecting young people's self-image.

Although AI cannot make aesthetic judgments, people may mistakenly accept its outputs as objective standards. This highlights the immense responsibility of those who design and train AI. The results depend on who provides the data, how it is filtered, and what criteria are used. If sexual objectification, racial bias, or gender stereotypes are present in the training data, AI may replicate or even amplify them.

At the same time, AI can offer creative combinations that humans might not have imagined. Using tools like GANs, AI can experiment by merging styles from different cultures or blending traditional and futuristic aesthetics. AI is a tool, and it is crucial to remember that the final judgment must still rest with humans.

While discussing AI’s limitations, we can also raise philosophical questions about the nature of “understanding.” Is AI’s imitation and statistical prediction of beauty a form of understanding? Or does true understanding require the complex interplay of human emotions and intentions? These are essential questions that call for humanistic reflection in tandem with technological advancement.