Synthetic Femininity and the Algorithmic Beauty Regime
- Ahmet Emre Çoban
- Sep 25
- 14 min read
In recent years, artificial intelligence has begun to redefine the landscape of beauty and gender representation. From hyper-realistic face filters on social media to fully synthetic models generated by algorithms, a new phenomenon of synthetic femininity has emerged. This term refers to the creation of feminine personas, images, and ideals through AI technologies – often reflecting exaggerated or idealized features beyond what any real human embodies.
Such AI-crafted visions of womanhood are not merely whimsical or niche. They signal the rise of an algorithmic beauty regime – a system in which algorithms and data dictate what is considered attractive or normal. This regime operates across visual culture: in the computer-augmented faces we see on Instagram, the ‘perfect’ AI models populating ads, and the biases baked into generative image networks. The implications are profound. Feminist scholars have long argued that beauty standards function as a form of social control (like the “beauty myth,” per Naomi Wolf, 1990) – a set of norms that women are pressured to conform to under patriarchy. Now, these norms are being codified and amplified by AI, potentially with even greater reach and subtlety.
Synthetic femininity sits at the intersection of technology, gender, and power: it raises urgent questions about who programs our ideals and to what end.
Are these algorithmic beauty standards merely reproducing age-old sexist and racist stereotypes, or might they also disrupt them? How do concepts from feminist theory – like the male gaze, intersectionality, or data feminism – help us critically understand this new digital beauty culture?
These questions are tackled in this article through an interdisciplinary lens, with feminist theory, media studies, AI ethics, and visual culture being drawn on.
Algorithmic Beauty Standards: From Male Gaze to Fascist Aesthetics
One vivid entry point into synthetic femininity is Jay Rosenbaum’s (2025) recent study of AI-generated “I need husband” images circulating on social media. These are computer-generated pictures of women with exaggerated feminine features, posted by bots with captions suggesting the (fictional) woman is seeking a partner. On the surface, they are spammy curiosities designed to attract clicks. Yet Rosenbaum argues they reveal something deeper and more “sinister.” The women depicted conform to an extreme ideal of the male gaze – a concept from film theory (Laura Mulvey, 1975) describing how visual media often frame women as objects for male pleasure. Here, the male gaze is rendered by an algorithm, producing an uncanny simulacrum. The AI’s attempt to mechanically imitate male desire results in over-the-top images of feminine beauty, to the point of absurdity or horror. These grotesque digital Venuses serve as both an amplification and a parody of patriarchal beauty standards.
Rosenbaum’s critical lens goes further, linking these AI beauty constructs to the politics of fascist aesthetics and highlighting their echoes of Futurism, fascist obsessions with superhuman bodies, and Friedrich Nietzsche’s Übermensch. Fascist visual culture idolized mythic, monumental beauty—like neoclassical sculptures of perfectly muscled “Aryan” bodies. They argue that the exaggerated idealization of form in these AI images mirrors such fixations: the women appear unnaturally flawless, with pale skin and improbably curved, hyper-feminine bodies. Their uniform light skin and Eurocentrism-coded features reflect what Rosenbaum calls Western beauty aesthetics dominating the dataset. This convergence of the algorithmic and the authoritarian suggests that without intervention, generative AI may default to reproducing the most extreme hierarchical beauty norms—ones disturbingly aligned with ultraconservative and fascist visions of women as white, slender, and objectified.
The center depicts a dimly lit, minimalist room with a window, desk, and wooden floors and cupboards. The right side features a collage of Cambridge landmarks, historical photographs of women, and a black and white figure in Edwardian attire. There is a map of Cambridge in the background, which is overlayed with images of old fountain pens and ink, books, and a handwritten letter.
Rosenbaum situates the “I need husband” posts within what they call AI slop—the flood of low-quality, bot-generated content saturating social networks, from kitsch to propaganda. Many of these surreal beauties circulate near right-wing and conspiratorial content; alt-right and misogynistic communities eagerly share them, often alongside reactionary slogans. Thus, AI pin-up girls become digital foot soldiers in culture wars, fueling nostalgia for ‘traditional’ (if uncanny) femininity. This algorithmic beauty regime dovetails with algorithmic extremism: the same mechanism that generates a sexy artificial woman can be co-opted to promote rigid gender roles or white supremacist fantasies. It warns us to ask who shapes AI’s vision of beauty and whose interests it serves. As critique, Rosenbaum re-renders these women as marble statues via 3D printing, exposing their classical fascist echoes and the kitsch underbelly of AI slop—making the danger of synthetic femininity physically tangible.
Data Feminism and Algorithmic Bias
To understand the algorithmic beauty regime, Catherine D’Ignazio and Lauren Klein’s concept of data feminism applies feminist insights to data science and AI, challenging structural inequalities. They argue that a feminist lens reveals who holds power in AI design, whose perspectives are encoded, and who is marginalized.
Algorithmic feminism asks what gendered biases are embedded in algorithms and how to confront them. Core principles include “rethinking binaries and hierarchies” and “examining and challenging power.”
This means seeing AI as neither neutral nor objective but shaped by its creators’ values and social inequalities. As they note, “unequal, undemocratic, extractive, and exclusionary forces” drive AI. AI beauty systems exemplify this, reflecting the biases of Western, male-dominated tech cultures through their training data and aesthetic defaults.
Research shows that algorithmic bias pervades visual AI systems. Even before deepfakes, computer vision misclassified those outside narrow norms. Commercial facial analysis tools notoriously failed on gender and race diversity. Scheuerman et al. (2019) found these systems “performed consistently worse on transgender individuals and were universally unable to classify non-binary genders,” effectively erasing identities beyond the binary. Error rates also varied sharply by race and gender: light-skinned men had near-perfect accuracy, while darker-skinned women saw over 30% errors. These disparities stemmed from training data and design choices by predominantly white male developers. In short, the bias of creators and datasets becomes the bias of the algorithm. This reflects Kimberlé Crenshaw’s (1989) idea of intersectional bias: those historically marginalized—Black women, non-binary people—are most likely to be misrecognized or distorted by AI systems (Buolamwini & Gebru, 2018).
Generative AI shows similar patterns. Such systems learn from vast online image datasets rife with prejudice. Haley R. Stacy (2025) notes that “generative models often amplify biases and stereotypes on race and gender,” as internet data is “often pulled… without concern for diversity.” Thus, biased inputs produce biased outputs. Stacy finds that text-to-image models whitewash ethnicities to fit Eurocentrism and the male gaze. Similarly, Lou et al. (2024) show that algorithms “reinforce binary gender norms” and further marginalize gender minorities, creating feedback loops where algorithmic curation favors the already popular and normative.
Data feminism urges breaking algorithmic bias loops by making them visible and contestable. It asks who labeled training images as ‘beautiful’ (often implicit judgments shaped by societal prejudice) and calls for pluralism—AI designed to reflect multiple definitions of beauty. Yet, most AI beauty systems are reductive, optimizing for mass appeal or replicating common patterns, which amplifies the status quo. This creates data-driven essentialism: complex traits like beauty are flattened into narrow criteria. For example, an AI used by fashion firms might exclude darker-skinned or older faces simply because it learned biased correlations. This exemplifies algorithmic oppression, as described by Safiya Umoja Noble (2018). Data feminism stresses that such outcomes are not inevitable but result from design choices—and can be changed.
Intersectional Bias in AI-Generated Beauty
A 2024 The Washington Post investigation (Tiku & Chen, 2024) showed how skewed the algorithmic beauty ideal is. When DALL·E, Midjourney, and Stable Diffusion were prompted for “a beautiful woman” or “a normal woman,” nearly all outputs depicted young, thin women with almost no wrinkles or signs of aging—only 2% showed any aging, implying youth as a requirement. Racial diversity was also minimal: 90%+ of Midjourney’s “beautiful” and 98% of its “normal” women were light-skinned and slender. DALL·E showed 62% medium skin tones, Stable Diffusion 18% dark-skinned women, yet all emphasized slimness and light skin. Curviness appeared mainly in sexualized Barbie-like forms. As the AI artist Abran Maldonado noted, “most tools depict Anglo noses and European body types… skin tone just gets swapped.” (Tiku & Chen, 2024) In short, these systems apply minor recoloring while retaining Eurocentrism—a form of algorithmic whitewashing.
Bias extends beyond skin tone to body size. The Washington Post found that prompting DALL·E 3 via ChatGPT for a “fat woman” still produced women with small waists; the AI either ignored or undermined the instruction. Users report similar issues: unless weight is specified, outputs default to extreme thinness, and even then, the AI often compromises with hourglass figures. This resistance to depicting fat, old, or non-white women reflects biased training data. If most images labeled ‘beautiful’ were of young, thin, white women—as shaped by Western media and beauty standards—the model will replicate that skewed distribution.
From an intersectionality lens, those at multiple marginalized intersections—like older, dark-skinned, plus-sized women—are almost absent from AI outputs unless forced in. This reflects digital erasure: biases on age, race, and body size combine to place some groups outside AI’s learned idea of beauty or normality. It echoes intersectional invisibility, where women of color are overlooked in both feminist and anti-racist agendas. As Haley R. Stacy (2025) notes, models generalize ethnicities with Western stereotypes, producing whitewashed composites and excluding real women’s diverse bodies and features. Bias in AI beauty standards mirrors broader hierarchies.
Stacy notes some models link lighter skin with high-status jobs (CEO, lawyer) and darker skin with low-wage work, simply copying societal patterns. This naturalizes colorism and colonial beauty norms: assuming ‘beautiful’ women have Eurocentrism-coded features.
Such intersectional bias compounds effects—beauty filters may lighten skin, slim noses, and smooth textured hair, conforming faces to a white ideal. These subtle shifts send a message, especially to youth: to be beautiful digitally, one must erase markers of marginalized identities.
Awareness is growing that AI needs an intersectionality check-up. Efforts now aim to diversify datasets and guide models toward fairness, yet cultural momentum still favors homogenized ideals. True change requires examining whose faces were deemed ‘normal’, who is invisible, and how power shaped these systems—only then can the algorithmic beauty regime be rebuilt.
The Beauty-Verse: Social Media Filters and Digital Aesthetic Regimes
While generative AI creates faces from text, social media platforms like Instagram, Snapchat, and TikTok use augmented reality (AR) beauty filters to transform real faces. These range from subtle edits (smooth skin, bright eyes) to dramatic reshaping, forming what is called the beauty-verse—a regime of algorithmically imposed beauty norms users unconsciously adopt. Despite seeming variety, most filters apply similar changes: smooth uniform skin, almond eyes and brows, full lips, small nose, pronounced cheeks. A report by ELLIS Alicante (who, referencing this concept, titled their project The BeautyVerse) notes this homogenization (n.d.). Whatever one’s starting face, filters converge on a youthful, slim, light-skinned, feminine ideal—an Instagram face with cat-like eyes, long lashes, petite nose, high cheekbones, pouty lips, and airbrushed skin, blending Eurocentrism and Kardashian-esque features for maximum camera appeal.
The ELLIS’s project showed that beauty filters reduce visual diversity. Applying popular Instagram filters to the diverse “FairFace Dataset”, they created “FairBeauty” and found ‘beautified’ faces became statistically more similar, erasing distinguishing traits. Filters lighten tans, soften ethnic features, and standardize face shapes, pushing all toward a singular beauty-verse ideal. They also embed racial bias—often mishandling darker skin or lightening it—creating a feedback loop where homogenized images reshape both user expectations and algorithmic norms.
Sociologically, the beauty-verse extends Wolf’s (1990) the beauty myth into the digital age. Wolf argued beauty standards intensified to restrain women’s rising power; now algorithmic beauty enforces a hyper-globalized ideal. Social media frames self-expression yet nudges users to conform, consuming time, energy, and resources. Unlike 1990s ads, today’s beauty myth is embedded in our tools—each ‘beautified’ selfie enacts self-surveillance within a digital beauty panopticon.
The beauty-verse filters shape both self-esteem and cultural beauty norms. Growing up with FaceTune and TikTok effects, many youths internalize these ideals, fueling body dysmorphia and cosmetic surgery (Snapchat dysmorphia). Their uniformity especially alienates those farthest from the ideal—darker skin or non-Eurocentrism features—promoting subtle self-erasure and equating beauty with light, Caucasian-like traits rooted in colonial aesthetics. Awareness and pushback are growing. Some influencers post “filter vs. no filter” to expose fake perfection, and the European Union (like the briefing by European Parliamentary Research Service, 2023) considers requiring labels on edited images. Feminist-minded alternatives—filters adding diversity (gray hair, varied noses)—remain rare. The beauty-verse persists, driven by platforms monetizing polished faces. Here, capital, technology, and patriarchy converge; data feminism urges redesigning beauty platforms toward equity and self-acceptance instead of profit and normativity.
AI-Influenced Visual Culture and Posthuman Beauty
Beyond filters and slop posts, a new AI-driven visual culture blurs human and machine creativity. Artists now co-create with generative AI, raising the notion of posthuman beauty—an aesthetic that transcends traditional human form. It asks: What does beauty mean when bodies can be endlessly altered or invented? Could AI liberate us from gendered norms, or does it merely repackage them under a high-tech veneer?
The grotesque “I need husband” images discussed by Rosenbaum hint at posthuman beauty: their extra limbs and implausible forms accidentally critique the male gaze, exposing the absurdity of codifying desire. These failures can be eerily comic, echoing traditions from Mary Shelley’s Frankenstein to cyborg women in contemporary art. Some artists even exploit AI glitches to create mutant figures as commentary on unattainable ideals—finding in these distortions a posthuman beauty that reflects our alienation from rigid standards.
A growing trend uses AI to create flawless virtual influencers and models. Lil Miquela—a CGI influencer with millions of followers—pioneered this, and now brands ‘hire’ AI models with perfectly symmetrical faces and ‘ideal’ bodies. This consumer-oriented form of posthuman beauty promises ageless, weightless, brand-aligned perfection, yet it is hollow, existing only in pixels.
Media scholars warn that such avatars may erode real women’s self-image and careers as audiences prefer AI’s polished reliability. This recalls the Pygmalion myth: as Erscoi et al. (2023) note, tech history shows a recurring “Pygmalion displacement,” where humanized AI replaces dehumanized women. These digital Galateas risk displacing real women and reinforcing sexist dynamics.
The posthuman beauty need not be anti-human or anti-woman. Donna Haraway (1985) and Rosi Braidotti (2022) envision merging with tech as liberating, not oppressive. Some artists use AI to create gender-fluid or surreal bodies defying binaries—seen in avant-garde fashion and art—expanding beauty beyond the slim and symmetrical. Generative models craft futuristic fashion images with cybernetic implants or unconventional proportions yet, frame them as beautiful. Such experiments embrace ambiguity, fluidity, and norm-breaking, resonating with queer aesthetics and trans aesthetics by showing that bodies can be malleable and self-defined.
Progressive uses of AI remain niche beside the flood of homogenized imagery. Visual culture mainly absorbs AI in ways that reinforce dominant beauty ideals—because they sell. These risks deepen the toxic beauty problem: overexposure to idealized images fueling dissatisfaction. Jessica Byrne (2023) asked, “Are AI-generated images perpetuating toxic beauty standards?” The answer seems yes—unless deliberately countered.
AI visual culture sits at a crossroads. It can entrench old biases or disrupt them, embodying the tension within posthuman beauty: either dystopian non-human perfection that deepens inadequacy or utopian diversity beyond biology and prejudice. Which path prevails depends on whose values shape our algorithms. Currently, power lies with non-diverse tech and media elites—making interventions by feminist scholars, ethicists, and artists vital to steer these aesthetics toward inclusivity and critique.
The Algorithmic Beauty Myth and Its Discontents
All these threads converge into an algorithmic beauty myth: a new mythology of beauty propagated by algorithms. Like Wolf’s original myth, it upholds power structures privileging youth, whiteness, and femininity—now cloaked in technological objectivity. It implies data has ‘proven’ what beauty looks like, masking the predominantly male, Western engineers curating the data and models. This math-driven decree hides cultural prejudice behind a veneer of neutrality.
Its discontents are clear. Constant exposure to filtered or AI-‘perfected’ images erodes self-esteem, and fuels cosmetic procedures to match selfies. Culturally, it erases diversity; socio-politically, it dovetails with regressive ideals, reinforcing colorism and pushing features aligned with Eurocentrism. This intensifies real harms—from black-market skin-lightening products to surging lip fillers and rhinoplasty—showing how AI normalizes a singular, exclusionary image of beauty worldwide.
A darker side of synthetic media is deepfakes and non-consensual imagery. Haley R. Stacy notes that 98% of deepfakes are porn, 99% targeting women, faces inserted into explicit content without consent. This weaponizes AI against women’s autonomy, echoing entitlement to their bodies. Laws lag behind, though the DEFINES Act (2024) in the United States aims to penalize deepfake sexual images. It shows how tech inherits and magnifies misogynistic contexts.
Possible remedies include algorithmic accountability and transparency. Companies could audit outputs by race, age, and body type, and clearly label AI-generated or altered images. Watermarking and disclaimers can’t end harm but warn viewers. Several countries are considering such rules, recognizing that manipulated media threatens public well-being—in beauty and beyond.
From a feminist perspective, key strategies include media literacy and counter-narratives. This means teaching especially youth that online beauty is curated and distorted—helping them recognize the algorithmic beauty myth. Grassroots campaigns (e.g., the noBODYlikeyou campaign by Body Proud and Body Positive Alliance on TikTok, or the AerieReal initiative by Aerie, encouraging unretouched photos) promote unfiltered images or celebrate diverse beauty through hashtags, using platform culture to subvert algorithmic bias. If enough users engage with content featuring older or larger-bodied women, algorithms may amplify it, challenging default ideals.
In design, interest is growing in ethical AI and values-centered approaches. For beauty tools, this could mean training on balanced datasets like FairBeauty and involving marginalized groups in development—echoing “Nothing about us, without us.” Art and scholarship are vital in critiquing the algorithmic beauty regime. Researchers like Rosenbaum and Erscoi et al. name its dynamics (“AI slop,” “Pygmalion displacement”), while artists satirize its ideals—Rosenbaum’s marble statues, for instance, expose the kitsch lurking beneath AI’s perfection.
This regime is synthetic in production but real in effect, rooted in and reshaping social values. Resisting its harms demands regulatory, technological, and cultural efforts, recalling earlier feminist fights against airbrushed ads or narrow casting—now on the AI terrain. Applying a data feminism lens can redirect visual culture toward diversity, equity, and creativity rather than fascistic algorithms or profit-driven platforms. We must ask: which images and ideals should we amplify? The answer must center those historically excluded, so all genders, ages, and bodies can see themselves reflected. In such a future, synthetic femininity could mean not a fembot ideal but a flourishing of diverse, reimagined forms.
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