Motherhood in the Cameraless Age
In May 2022, USA Vogue published a cover image that seemed to vibrate with a kind of regal, crimson defiance. Rihanna stood before a pair of opulent, gold-adorned doors, wearing a skin-tight red lace bodysuit and stilettos. Her hands were cupped beneath her pregnant belly—a stark, intensely visible performance of maternity that the media industry promptly categorized as "branding the bump". To the casual observer scrolling through digital feeds, it was a moment of supreme post-feminist liberation. To those of us who study the visual economy, however, the cover was a beautifully staged battleground.
For nearly thirty thousand years, the maternal body has been one of human culture’s most heavily policed visual commodities, spanning from the earthbound curves of the Aurignacian Venus in 30,000 BCE to contemporary magazine covers. Today, under the reign of what sociologists call "disciplinary neoliberalism" and the "new momism," the silent mother has been replaced by a hyper-visible alternative: the "yummy mummy". She is an aspirational figure who must perform "aesthetic labour"—constantly grooming, consuming, and displaying her body to prove she is "having it all"
Historically, the photograph was our primary receipt of this performance. In his 1981 masterpiece Camera Lucida, Roland Barthes maintained that the photograph and its referent are "glued together, limb by limb". To have a photograph of a mother, a physical woman had to stand before a physical lens, casting her shadow onto a light-sensitive surface. The image carried her physical presence with it, an objective referent tied to empirical reality.
But we are currently witnessing a profound ontological shift—one that is quietly decoupling the image from the camera altogether. With the integration of generative artificial intelligence, we have entered the era of what the scholar Yavuz (2023) calls "cameraless photography". AI-generated images are not captured; they are synthesized from a rhizomatic matrix of data and code. When we type text prompts into a model—terms like “single mum,” “yummy mummy,” “tradwife,” or “pregnant celebrity”—we are no longer documenting a lived life. We are assembling pixels. The traditional subject-object relationship of the camera is displaced, replaced by a synthetic simulation of what motherhood "should" look like.
The irony is that while the camera has been discarded, the system's ideological biases remain stubbornly intact. When we dissect the datasets that feed these generative algorithms, we quickly find that they are not neutral engines. Instead, as explored in the New York Times by Miller (2015), they encode existing social hierarchies, leading to "algorithmic discrimination". The algorithmic defaults of AI systems overwhelmingly privilege a very specific, normative ideal of the "good mother". They reproduce a soft-filtered, domestic bliss: affluent, heterosexual, slim, and overwhelmingly white.
This is the exact same visual hierarchy we see on physical newsstands. In our visual discourse analysis of Vogue covers, a clear racial dichotomy emerges. When white celebrity mothers like Sienna Miller or Victoria Beckham are featured, they are typically framed in soft, natural daylight. Sienna Miller is depicted on a serene beach, her gaze relaxed and contemplative—a portrayal of "natural conservatism" that positions white, middle-class pregnancy as the "correct" and unthreatening norm. The Beckhams are photographed in close physical proximity, surrounded by children and a family pet, devoid of sexual agency, reinforcing the idyllic, heteronormative nuclear family.
In contrast, mothers of colour are frequently subjected to a different visual regime. Rihanna and Naomi Campbell are cast under pronounced, dramatic, artificial lighting. Naomi is portrayed with untamed, abundant locks; Rihanna poses in opulent, regal settings or against a dramatic beach at dusk. While these images are undeniably powerful and grant these women immense sexual agency, they also navigate a complex "sexual economy" that risks exoticising and eroticising non-white maternal bodies, casting them as the "exotic other".
When we feed these exact archetypes into generative AI models, the output doesn't challenge these biases; it locks them in. Prompting systems with terms like 'single mum,' 'yummy mummy,' 'tradwife,' or black or white 'pregnant celebrities' reveals how generative AI replicates historical bias. The resulting synthetic images serve as a striking, physical testament to deeply entrenched socio-cultural stereotyping.
Rather than jumping to grand, sweeping conclusions, this exploration serves as a targeted, ongoing inquiry—an exploratory project investigating how algorithmic bias operates at the intersection of technology and maternal representation. It is not a definitive statement on the entirety of maternal lived experience, but a vital connection to the broader questions of digital mothering.
By utilizing Munn et al.'s (2023) framework of "unmaking" AI systems, this exploratory visual project seeks to critically interrogate three levels of algorithmic production:
Unmaking the Ecosystem: Questioning the business models and normative beauty ideals encoded by the optimization-driven builders of these technologies.
Unmaking the Data: Interrogating how maternal identities are represented or erased in training datasets, specifically noting how algorithmic defaults privilege affluent, white "tradwife" aesthetics while marginalizing alternative maternal scripts like single mothers, queer parents, or working-class families.
Unmaking the Output: Critically analyzing the AI-generated imagery itself to map the contradictions between real, lived maternal subjectivities and the sterile perfection of algorithmic imagination.
The ontological shift in photography is changing not only how images are made but also how we must analyze them. In a world increasingly saturated with synthetic visual media, this exploratory work on algorithmic bias does not offer easy solutions. Instead, it raises a necessary question: as the physical camera is displaced by the pixel, how do we prevent these new machines from permanently cementing the old biases of the spectacle?
This exploratory project is part of my ongoing research into the visual economy of motherhood. Explore the full dataset and analytical breakdowns on the AI Visual Analysis Project Page.
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