Over the past year, artificial intelligence has entered our everyday lives not only through abstract debates, but also in the form of our own faces. Out of Ghibli style From portraits and cinematic headshots to tropical beach edits and “professional” profile photos, AI-generated images created using tools like Lensa, Remini, and FaceApp have become a popular way to see and show ourselves in online spaces. These images spread quickly and collect likes, comments and endorsements. For many, they offer a small moment of joy, confidence or visibility.
But behind this seemingly innocuous trend lies a deeper question: If beauty is subjective, who decides what kind of “beauty” AI produces, and how does the product it produces seem so affirming?
AI learns beauty from a biased world
AI doesn’t imagine beauty alone. It learns from data: billions of images uploaded, tagged, rated, liked and shared on the internet. These data sets predominantly reflect existing social hierarchies. Research on large image datasets used to train generative AI, including the Study on Gender Shades by Joy Buolamwini and Timnit Gebru shows a consistent over-representation of lighter skin tones, slender bodies, youthful faces, and an upper-class urban aesthetic. Images associated with “beauty,” “success,” or “professionalism” are far more likely to depict light-skinned, physically attractive, and attractive people as portrayed in popular culture.
When these images are created, they do not represent creativity, but rather they repeat what the internet and society have already confirmed as “beautiful.”
When these images are created, they do not represent creativity, but rather they repeat what the Internet has already confirmed as “beautiful.” The subjective and culturally specific standards of beauty that have found a place in people’s popular imagination. AI-generated “beautiful” images risk slipping back from the mainstream and becoming flattened into something more universal and neutral. Beauty parameters were never just about aesthetics; It was a tool of discipline and shaped who was considered worthy, respectable, or desirable.
What happens when you upload your face?
Most users find AI imaging tools to be magic: upload a photo, wait a few seconds, and get a polished version of yourself. But behind this seamless interface lies a process worth understanding. Generative AI models, including those powering Midjourney, Stable Diffusion and DALL-E, are trained on billions of images sourced from the internet, social media posts, archival photos and artwork, often without the knowledge or consent of the people depicted in them. Studies on data sets such as LAION-5B have uncovered the inclusion of private photos, medical images and personal content that people never wanted to share. AI doesn’t “see” beauty. It detects statistical patterns: which features tend to appear together, which lighting correlates with “professional,” and which skin texture is considered “flawless.” If it “beautifies” your face, it doesn’t express a creative opinion. It brings your facial features toward a mathematical average of everything you’ve learned to call beautiful.
This process takes place in what engineers call “latent space,” an abstract map where your face is reduced to a set of coordinates. An improvement means moving these coordinates closer to the cluster marked as “ideal”. The result feels personal but is deeply impersonal: her face filtered through the aesthetic preferences of millions of strangers whose images trained the model. You won’t be seen. They are optimized.
Consent that we never gave
There is another layer that most users don’t consider. When you upload your photo, you often grant the company broad rights to store, modify and reuse it, sometimes to train future models, sometimes to distribute it to third parties. These permissions are hidden in terms of service that few people read and that most find confusing and concerning widely reported when Lensa’s “magical avatars” went viral. Your face becomes raw material, fed back into the system that shapes the next user’s “enhancement.” Some apps extract something called “face embedding”: a mathematical fingerprint unique to your face that can identify you across images and platforms and, similar to technology, can persist long after the original photo is deleted Used by Clearview AI Removing billions of faces from social media without consent.
The same technology that makes you look like a Ghibli character is the same technology that enables deepfakes, AI-generated images that insert real faces into made-up scenarios without consent. Research by Sensity AI shows that over ninety percent of deepfake content online is non-consensual pornography and is predominantly aimed at women. The tools are not separate. They share the same technical architecture, embedded in different interfaces. The question is not just whether the image looks attractive. It’s about what we feed, who else can use it, and whether clicking “I agree” constitutes meaningful consent when we don’t understand what we’re agreeing to.
Presenting the “beautiful” self online
Many people perceive using AI-generated beauty images as something they do voluntarily and for fun. They experiment with versions of ourselves, adjusting angles and facial expressions and choosing the image that feels “right.” But this choice takes place within an algorithmic environment that, as in the offline world, constantly rewards conformity.
Over time, these signals become internalized. They begin to anticipate what they will like, what will be shared, what will be ignored. This is a form of self-surveillance where validation depends on how well we conform to the prevailing aesthetic. Happiness is equated with visibility; Visibility with compliance.
AI imaging tools are often celebrated for making beauty accessible to everyone. Editing software and professional photography, once reserved only for the privileged, are now accessible to anyone with a smartphone. This narrative is encouraging, especially in a world where access to resources is deeply unequal.
Accessing tools does not automatically result in a power outage. AI-enhanced images that match conventional beauty standards get higher visibility and validation. Users quickly learn what works and what doesn’t. The result is no freedom from beauty standardsbut rather their implementation on a large scale.
However, access to tools does not automatically lead to loss of power. AI-enhanced images that match conventional beauty standards You get higher visibility and validation. Users quickly learn what works and what doesn’t. The result is no freedom from beauty standardsbut rather their implementation on a large scale.
Furthermore, beauty is not the same everywhere. In India alone, ideals of beauty change dramatically between regions, caste affiliations, class positions and cultural contexts. What is considered attractive in one district may no longer be considered attractive a few hundred kilometers away. Skin color, body shape, clothing and even posture have different meanings depending on social location.
Yet AI-generated beauty often reflects a globalized, elitist aesthetic associated with wealth and mobility. Regional features, darker skin tones, non-normative bodies, and local styles are either erased or subtly corrected when using AI.
This is no coincidence. AI systems trained on global datasets reproduce what is most visible and profitable online. What seems like a universal standard is actually the taste of the privileged sector, dispersed and expanded by technology.
A missed feminist opportunity
Feminist scholars who study data and technology have long argued that AI could be designed differently, with a more conscious gender perspective. In the case of beauty, AI could have made visible the diversity and multidimensionality of beauty rather than reinforcing hegemonic beauty norms. It could reflect multiple, context-specific aesthetics rather than focusing on a single ideal. It could have even shown how beauty standards change over time, geography and social location.
It could have become an opportunity for bodies not traditionally considered “beautiful” to be given space to showcase themselves. It could have opened up discussions about how love, happiness, honesty and qualities like these influence whether someone appears beautiful or ugly. The same face can look most beautiful or ugliest depending on how it reacts to situations or what its ideals are. Conversations like this could have been initiated by AI.
It is understandable that AI cannot act alone; It is society that determines how it functions. In that sense, this becomes a missed opportunity. Hegemonic beauty works well because it is familiar and easily consumable. For many users, engaging with AI-generated images brings joy, being seen, being admired, and momentarily escaping the uncertainties of a harsh world. These feelings are real and should not be dismissed. But they exist within a system that offers conditional agency, that is, visibility in exchange for conformity. AI, as it currently works, offers moments of affirmation while silently reproducing the very hierarchies that feminism seeks to dismantle.
The question we need to ask is not whether people should stop using AI-generated images. It’s about whether we are willing to challenge the systems that determine whose beauty is enhanced and whose is excised. If AI shapes our self-image, feminists and gender scholars must insist on reshaping AI itself. Otherwise, we risk confusing smooth skin and cinematic lighting with progress without addressing the deeper structures of exclusion that remain entrenched.
