The Ghost Stays in the Picture, Part 2: Data Casts Shadows

Eryk Salvaggio is a 2024 Flickr Foundation Research Fellow, diving into the relationships between images, their archives, and datasets through a creative research lens. This three-part series focuses on the ways archives such as Flickr can shape the outputs of generative AI in ways akin to a haunting. Read part one, or continue to part three.

“Today the photograph has transformed again.” – David A. Shamma, in a blog post announcing the YFCC100M dataset.

In part one of this series, I wrote about the differences between archives, datasets, and infrastructures. We explored the movement of images into archives through the simple act of sharing a photograph in an online showcase. We looked at the transmutation of archives into datasets — the ways those archives, composed of individual images, become a category unto themselves, and analyzed as an object of much larger scale. Once an archive becomes a dataset, seeing its contents as individual pieces, each with its own story and value, requires a special commitment to archival practices.

Flickr is an archive — a living and historical record of images taken by people living in the 21st century, a repository for visual culture and cultural heritage. It is also a dataset: the vast sum of this data, framed as an overwhelming challenge for organizing, sorting, and contextualizing what it contains. That data becomes AI infrastructure, as datasets made to aid the understanding of the archive become used in unexpected and unanticipated ways.  

In this post, I shift my analysis from image to archive to dataset, and trace the path of images as they become AI infrastructure — particularly in the field of data-driven machine learning and computer vision. I’ll again turn to the Flickr archive and datasets derived from it.

99.2 Million Rows

A key case study is a collection of millions of images shared in June 2014. That’s when Yahoo! Labs released the YFCC100M dataset, which contained 99.2 million rows of metadata describing photos by 578,268 Flickr members, all uploaded to Flickr between 2004 and 2014 and tagged with a CC license. The dataset contained information such as photo IDs, URLs, and a handful of metadata such as the title, tags, description. I believe that the YFCC100M release was emblematic of a shift in the public’s — and Silicon Valley’s — perception of the visual archive into the category of “image datasets.” 

Certainly, it wasn’t the first image dataset. Digital images had been collected into digital databases for decades, usually for the task of training image recognition systems, whether for handwriting, faces, or object detection. Many of these assembled similar images, such as Stanford’s dogs dataset or NVIDIA’s collection of faces. Nor was it the first transition that a curated archive made into the language of “datasets.” For example, the Tate Modern introduced a dataset of 70,000 digitized artworks in 2013.  

What made YFCC100M interesting was that it was so big, but also diverse. That is, it wasn’t a pre-assembled dataset of specific categories, it was an assortment of styles, subject matter, and formats. Flickr was not a cultural heritage institution but a social media network with a user base that had uploaded far more images than the world’s largest libraries, archives, or museums. In terms of pure photography, no institution could compete on scale and community engagement. 

The YFCC100M includes the description, tags, geotags, camera types, and links to 100 million source images. As a result, we see YFCC100M appear over and over again in papers about image recognition, and then image synthesis. It has been used to train, test, or calibrate countless machine vision projects, including high-rated image labeling systems at Google and OpenAI’s CLIP, which was essential to building DALL-E. Its influence in these systems rivals that of ImageNet, a dataset of 14 million images which was used as a benchmark for image recognition systems, though Nicolas Maleve notes that nearly half of ImageNet’s photos came from Flickr URLs. (ImageNet has been explored in-depth by Kate Crawford and Trevor Paglen.)

10,000 Images of San Francisco

It is always interesting to go in and look at the contents of a dataset, and I’m often surprised how rarely people do this. Whenever we dive into the actual content of datasets we discover interesting things. The YFCC100M dataset contains references to 200,000 images by photographer Andy Nystrom alone, a prolific street photographer who has posted nearly 8 million images to Flickr since creating their account in 2008. 

The dataset contains more than 10,000 images each of London, Paris, Tokyo, New York, San Francisco, and Hong Kong, which outnumber those of other cities. Note the gaps here: all cities of the Northern hemisphere. When I ask Midjourney for an image of a city, I see traces of these locations in the output. 

Are these strange hybrids a result of the prevalence of Flickr in the calibration and testing of these systems? Are they a bias accumulated through the longevity of these datasets and their embeddedness into AI infrastructures? I’m not confident enough to say for sure. But missing from the images produced from the generic prompt “city” are traces of what Midjourney considers an African city. What emerges are not shiny, glistening postcard shots or images that would be plastered on posters by the tourist bureau. Instead, they seem to affirm the worst of the colonizing imagination: unpaved roads, cars broken down in the street. The images for “city” are full of windows reflecting streaks of sunlight; for “African city,” these are windows absent of glass. 

“A prompt about a ‘building in Dakar’ will likely return a deserted field with a dilapidated building while Dakar is a vibrant city with a rich architectural history,” notes the Senegalese curator Linda Dounia. She adds: “For a technology that was developed in our times, it feels like A.I. has missed an opportunity to learn from the fraught legacies that older industries are struggling to untangle themselves from.”

Beyond the training data, these legacies are also entangled in digital infrastructures. We know images from Flickr have come to shape the way computers represent the world, and how we define tests of AI-generated output as “realistic.” These definitions emerge from data, but also from infrastructures of AI. Here, one might ask if the process of calibrating images to places has been so centered on the geographic regions where Flickr has access to ample images: 10,000 images each from cities of the Northern Hemisphere. These created categories for future assessment and comparison. 

What we see in those images of an “African city” are what we don’t see in the data set. What we see is what is what is missing from that infrastructure: 10,000 pictures of Lagos or Nairobi. When these images are absent from the training data, they influence the result. When they are absent from the classifiers and calibration tools, that absence is entrenched.

The sociologist Avery Gordon writes of ghosts, too. For Gordon, the ghost, or the haunting, is “the intermingling of fact, fiction and desire as it shapes the personal and social memory … what does the ghost say as it speaks, barely, in the interstices of the visible and invisible?” In these images, the ghost is the image not taken, the history not preserved, the gaps that haunt the archives. It’s clear these absences move into the data, too, and that the images of artificial intelligence are haunted by them, conjuring up images that reveal these gaps, if we can attune ourselves to see them.

There is a limit to this kind of visual infrastructural analysis of image generation tools — its reliance on intuition. There is always a distance between these representations of reality in the generated image and the reality represented in the datasets. Hence our language of the seance. It is a way of poking through the uncanny, to see if we can find its source, however remote the possibility may be.  


We do know a few things, in fact. We know this dataset was tested for representativeness, that was defined as how evenly it aligned with Flickr’s overall content — not the world at large. We know, then, that the dataset was meant to represent the broader content of Flickr as a whole, and that the biases of the dataset — such as the strong presence of these particular cities — are therefore the biases of Flickr. In 2024, an era where images have been scraped from the web wholesale for training data without warning or permission, we can ask if the YFCC100M dataset reflected the biases we see in tools like DALL-E and Midjourney. We can also ask if the dataset, in becoming a tool for measuring and calibrating these systems, may have shaped those biases as a piece of data infrastructure.

As biased data becomes a piece of automated infrastructure, we see biases come into play from factors beyond just the weights of the training data. It also comes into play in the ways the system maps words to images, sorts out and rejects useful images, and more. One of the ways YFCC100M’s influence may shape these outcomes is through its role in training the OpenAI tool I mentioned earlier, called CLIP. 

CLIP looks at patterns of pixels in an image and compares them to labels for similar sets of pixels. It’s a bridge that connects the descriptions of images to words of a user’s prompt. CLIP is a core connection point between words and images within generative AI. Recognizing whether an image resembles a set of words is how researchers decided what images to include in training datasets such as LAION 5B. 


CLIP’s training and calibration dataset contained a subset of YFCC100M, about 15 million images out of CLIP’s 400 million total. But CLIP was calibrated with, and its results tested against, classifications using YFCC100M’s full set. By training and calibrating CLIP against YFCC100M, that dataset played a role in establishing the “ground truth” that shaped CLIP’s ability to link images to text. 

CLIP was assessed on its ability to scale the classifications produced by YFCC100M and MS-COCO, another dataset which consisted entirely of images downloaded from Flickr. The result is that the logic of Flickr users and tagging has become deeply embedded into the fabric of image synthesis. The captions created by Flickr members modeled — and then shaped — the ways images of all kinds would be labeled in the future. In turn, that structured the ways machines determined the accuracy of those labels. If we want to look at the infrastructural influences of these digital “ghosts in the machine,” then the age, ubiquity, and openness of the YFCC100M dataset suggests it has a subtle but important role to play in the way images are produced by diffusion models. 

We might ask about “dataset bias,” a form of bias that doesn’t refer to the dataset, or the archive, or the images they contain. Instead, it’s a bias introduced through the simple act of calling something a dataset, rather than acknowledging its constitutive pieces. This shift in focus shifts our relationship to these pieces, asking us to look at the whole. Might the idea of a “dataset” bias us from the outset toward ignoring context, and distract us from our obligation of care to the material it contains?  

From Drips Comes the Deluge

The YFCC100M dataset was paired with a paper, YFCC100M: The New Data in Multimedia Research, which focused on the needs of managing visual archives at scale. YFCC100M was structured as an index of the archive: a tool for generating insight about what the website held. The authors hoped it might be used to create tools for handling an exponential tide of visual information, rather than developing tools that contributed to the onslaught. 

The words “generative AI” never appear in the paper. It would have been difficult, in 2014, to anticipate that such datasets would be seen through a fundamental shift from “index” to “content” for image generation tools. That is a shift driven by the mindset of AI companies that rose to prominence years later.

In looking at the YFCC100M dataset and paper, I was struck by the difference between the problems it was established to address and the eventual, mainstream use of the dataset. Yahoo! released the paper in response to the problems of proprietary datasets, which they claimed were hampering replication across research efforts. The limits on the reuse of datasets also meant that researchers had to gather their own training data, which was a time consuming and expensive process. This is what made the data valuable enough to protect in the first place — an interesting historical counterpoint to today’s paradoxical claim by AI companies that image data is both rare and ubiquitous, essential but worth very little.  


Creative Commons licensed pictures were selected for inclusion in order to facilitate the widest possible range of uses, noting that they were providing “a public dataset with clearly marked licenses that do not overly impose restrictions on how the data is used” (2). Only a third of the images in the dataset were marked as appropriate for commercial use, and 17% required only attribution. But, in accordance with the terms of the Creative Commons licenses used, every image in the dataset required attribution of some kind. When the dataset was shared with the public, it was assumed that researchers would use the dataset to determine how to use the images contained within it, picking images that complied with their own experiments.  

The authors of the paper acknowledge that archives are growing beyond our ability to parse them as archivists. But they also acknowledge Flickr as an archive, that is, a site of memory: 

“Beyond archived collections, the photostreams of individuals represent many facets of recorded visual information, from remembering moments and storytelling to social communication and self-identity [19]. This presents a grand challenge of sensemaking and understanding digital archives from non-homogeneous sources. Photographers and curators alike have contributed to the larger collection of Creative Commons images, yet little is known on how such archives will be navigated and retrieved, or how new information can be discovered therein.”

Despite this, there was a curious contradiction in the way Yahoo! Labs structured the release of the dataset. The least restrictive license in the dataset is CC-BY — images where the license requires attribution. Nearly 68 million out of the 100 million images in the dataset specifically stated there could be no commercial use of their images. Yet, the dataset itself was then released without any restrictions at all, described as “publicly and freely usable.”  

The dataset of YFCC100M wasn’t the images themselves. It was the list of images, a sample of the larger archive that was made referenceable as a way to encourage researchers to make sense of the scale of image hosting platforms. The strange disconnect between boldly declaring the contents as CC-licensed, while making them available to researchers to violate those licenses, is perhaps evident only in hindsight.

Publicly Available

It may not have been a deliberate violation of boundaries so much as it was a failure to grapple with the ways boundaries might be transgressed. The paper, then, serves as a unique time capsule for understanding the logic of datasets as descriptions of things, to the understanding of datasets as the collection of things themselves. This was a logic that we can see carried out in the relationships that AI companies have to the data they use. These companies see the datasets as markedly different from the images that the data refers to, suggesting that they have the right to use datasets of images under “fair use” rules that apply to data, but not to intellectual property. 

This breaks with the early days of datafication and machine learning, which made clearer distinctions between the description of an archive and the archive itself. When Stability AI used LAION 5B as a set of pointers to consumable content, this relationship between description and content collapsed. What was a list of image URLs and the text describing what would be found there became pointers to training data. The context was never considered. 

That collapse is the result of a set of a fairly recent set of beliefs about the world which increasingly sees the “image” as an assemblage of color information paired with technical metadata. We hear echoes of this in the defense of AI companies, that their training data is “publicly available,” a term with no actual, specific meaning. OpenAI says that CLIP was trained on “text–image pairs that are already publicly available” in its white paper.

In releasing the dataset, Yahoo’s researchers may have contributed to a shift: from understanding online platforms through the lens of archives, into understanding them as data sources to be plundered. Luckily, it’s not too late to reassert this distinction. Visual culture, memory, and history can be preserved through a return to the original mission of data science and machine learning in the digital humanities. We need to make sense of a growing number of images, which means preserving and encouraging new contexts and relationships between images rather than replacing them with context-free abstractions produced by diffusion models. 

Generative AI is a product of datasets and machine learning and digital humanities research. But in the past ten years, data about images and the images themselves have become increasingly interchangeable. Datasets were built to preserve and study metadata about images. But now, the metadata is stripped away, aside from the URL, which is used to analyze an image. The image is translated into abstracted information, ignoring where these images came from and the meaning – and relationships of power – that are embedded into what they depict. In erasing these sources, we lose insight into what they mean and how they should be understood: whether an image of a city was taken by a tourism board or an aid agency, for example. The biases that result from these absences are made clear.

Correcting these biases requires care and attention. It requires pathways for intervention and critical thinking about where images are sourced. It means prioritizing context over convenience. Without attention to context, correcting the source biases are far more challenging. 

Data Casts Shadows

In my fellowship with the Flickr Foundation, I am continuing my practice with AI, looking at the gaps between archives and data, and data and infrastructures, through the lens of an archivist. It is a creative research approach that examines how translations of translations shape the world. I am deliberately relying on the language of intuition — ghosts, hauntings, the ritual of the seance — to encourage a more human-scaled, intuitive relationship to this information. It’s a rebuttal of the idea that history, documentation, images and media can be reduced to objective data. 

That means examining the emerging infrastructure built on top of data, and returning to the archival view to see what was erased and what remains. What are the images in this dataset? What do they show us, and what do they mean? Maleve writes that to become AI infrastructure, a Flickr image is pulled from the context of its original circulation, losing currency. It is relabeled by machines, and even the associations of metadata itself become superfluous to the goal of image alignment. All that matters is what the machine sees and how it compares to similar images. The result is a calibration: the creation of a category. The original image is discarded, but the residue of whatever was learned lingers in the system. 

While writing this piece, I became transfixed by shadows within synthetic images. Where does the shadow cast in an AI generated image come from? They don’t come from the sun, because there is no sunlight within the black box of the AI system. Despite the hype, these models do not understand the physics of light, but merely produce traces of light abstracted from other sources.

Unlike photographic evidence, synthetic photographs don’t rely on being present to the world of light bouncing from objects onto film or sensors. The shadows we see in an AI generated image are the shadows cast by other images. The generated image is itself a shadow of shadows, a distortion of a distortion of light. The world depicted in the synthetic image is always limited to the worlds pre-arranged by the eyes of countless photographers. Those arrangements are further extended and mediated as these data shadows stretch across datasets, calibration systems, engineering decisions, design choices and automated processes that ignore or obscure their presence.

Working Backward from the Ghost

When we don’t know the source of decisions made about the system, the result is unexplainable, mysterious, spooky. But image generation platforms are a series of systems stacked on top of one another, trained on hastily assembled stews of image data. The outcomes go through multiple steps of analysis and calibration, outputs of one machine fed into another. Most of these systems are built upon a subset of human decisions scaled to cover inhuman amounts of information. Once automated, these decisions become disembodied, influencing the results.

In part 3 – the conclusion of this series – I’ll examine a means of reading AI generated images through the lens of power, hoping to reveal the intricate entanglement of context, control, and shifting meanings within text and image pairs. Just as shadows move across the AI generated image, so too, I propose, does the gaze of power contained within the archives.

I’ll attempt to trace the flow of power and meaning through datasets and data infrastructures that produce these prompted images, working backwards from what is produced. Where do these training images come from? What stories and images do they contain, or lack? In some ways, it is impossible to parse, like a ghost whose message from the past is buried in cryptic riddles. A seance is rarely satisfying, and shadows disappear under a flashlight.

But it’s my hope that learning to read and uncover these relationships improves our literacy about so-called AI images, and how we relate to them beyond toys for computer art. Rather, I hope to show that these are systems that perpetuate power, through inclusion and exclusion, and the sorting logic of automated computation. The more we automate a system, the more the system is haunted by unseen decisions. I hope to excavate the context of decisions embedded within the system and examine the ways that power moves through it. Otherwise, the future of AI will be dictated by what can most easily be forgotten.  

Read part three here.


I would be remiss not to point to the excellent and abundant work on Flickr as a dataset that has been published by Katrina Sluis and Nicolas Malevé, whose work is cited here but merits a special thank you in shaping the thinking throughout this research project. I am also grateful to scholars such as Timnit Gebru, whose work on dataset auditing has deeply informed this work, and to Dr. Abeba Birhane, whose work on the content of the LAION 5B dataset has inspired this creative research practice. 

In the images accompanying this text, I’ve paired images created in Stable Diffusion 1.6 for the prompt “ street shadows.” They’re paired with images from actual Flickr members. I did not train AI on these photos, nor did I reference the originals in my prompts. But by pairing the two, we can see the ways that the original Flickr photos might have formed the hazy structures of those generated by Stable Diffusion. 

The Ghost Stays in the Picture, Part 1: Archives, Datasets, and Infrastructures

Eryk Salvaggio is a 2024 Flickr Foundation Research Fellow, diving into the relationships between images, their archives, and datasets through a creative research lens. This three-part series focuses on the ways archives such as Flickr can shape the outputs of generative AI in ways akin to a haunting.

“The Absence Becomes the Thing.”
– Rindon Johnson,
from The Law of Large Numbers

Every image generated by AI calls up a line of ghosts. They haunt the training data, where the contexts of photographs are reduced to the simplest of descriptions. They linger in the decisions of engineers and designers in what labels to use. The ghosts that haunt the generated image are hidden by design, but we can find them through their traces. We just need to know how to look.

As an artist, the images created by AI systems are rarely interesting to me solely as photographs. I find the absences that structure these images, and the stories told in the gaps, to be far more compelling. The images themselves recycle the tropes of their training data. By design, they lean into the most common patterns, changing the details like a lazy student changing the words of a plagiarized essay.

I don’t turn to generative AI for beautiful images. I look for evidence of ghosts.

What exactly is a ghost in an AI system? It’s a structure or decision that haunts information in barely discernible, even invisible, ways. Datasets are shaped by absences, and those absences shape the image. As a diffusion model seeks the path to an image, the absence of pathways constrains what is possible. We can read these paths by looking at AI images critically, addressing the negative space of what appears on our screens. Who are the people we don’t see? What are the stories these images cannot tell?

This can mean absences in representation. When we have thousands of photographs of white children tagged as “girls,” but few black children, black girls are absent from the images. Absence haunts the generated image, shaping it: we will see mostly white girls because black girls have been pushed to the edges. This is not just a glib example. The exact scenario is precisely what I found when I analyzed a dataset used for training image generation tools and automated surveillance systems in 2019. The pattern holds today. Victorian-era portraits of white girls are prevalent in the training data for generative AI systems such as Stable Diffusion. Black girls are absent, with highly sexualized images of adult women taking their place.

Infrastructure makes ghosts, too. We build complex systems one step at a time, like a set of intersecting hallways. Artificial Intelligence is, at its heart, a means of automating decisions. They carry decisions from the past into the future. Once we inscribe these decisions into code, the code becomes infrastructure, subsumed into a labyrinth made by assembling the code of others and code yet to be written. As we renovate these structures through new code or system upgrades, the logic of a particular path is lost. We may need to build new walls around it. But when we bury code, we bury decisions beneath a million lines of if/then statements, weights, and biases of machine learning. Unchallenged, the world that has slipped past us shapes the predictions of these systems in ways we cannot understand.

This is true of most data driven, automated systems, whether we are talking about resume filters or parole decisions. For the generated photograph, these decisions include how we test and calibrate image recognition systems, and how we iterate upon these systems with every new model launch and interface.

Diffusion models — at the core of image generation systems — are an entanglement of systems. It relies on one system to label images, examining how pixels are clustered and matching them with human descriptions. We relied on underpaid labor by humans to test these systems by comparing the results of that tool to what they saw themselves. These comparisons are recorded and integrated into the memory of the model. The actions of those people were fused into the infrastructure of the model, shaping decisions long after they stopped working on the dataset.

We tend to make up stories about synthetic images based on what’s depicted. That is the work of the human imagination: a way of making sense of media based on references we’ve seen before. That is a ghost story, too. But if we want to meet the ghosts that shape AI-generated images, we have to dig deeper into the systems that produce them. The AI ghost story is a story of the past reaching into the present, and to understand it, it helps to know the lineage of those decisions.

Image synthesis has a history, and that history lingers in black boxes of the neural nets as they shape noisy pixels into something recognizable. Part of that story is the datasets, but data is applied to a vast number of uses. One of those uses is training larger systems to sort and handle larger sums of data.

Data shapes data infrastructure. From small sets of data, patterns are found and applied to larger sums of data. These patterns are repeatedly invoked whenever we call upon these systems into the future. The source data is always an incomplete model of things. But nonetheless, it is applied to larger datasets, which inherit and amplify the gaps, absences, and decisions from the past.

This is part of my creative research work on the seance of the digital archive. It focuses not only on data, but the lineage of data and the decisions made using that data to shape larger systems. A key piece of this lineage, and one that merits deeper exploration, is Flickr.

The Archive and the Dataset

With the rise of generative AI, vast troves of visual, textual, and sonic cultural heritage data have been folded into models that create new images, text, even music. But images are host to a special kind of spectral illusion. Most images shared online were never intended to become “data,” and in many ways, this transformation into data is at odds with the real value at the heart of what these archives preserve.

What is the difference between an archive and a dataset? We are dealing with many levels of abstraction here: an archive consists of individual objects designed to serve some human purpose. These objects may then be curated into a collection. It may be a collection of pamphlets, political cartoons, or documentary photographs. It may be the amateur photographer aiming to preserve a snapshot of a birthday party whose daughter and granddaughter celebrated alongside one another. Flickr, as a photo-sharing website, is host to all of these. The miracle of data, compression, and the world wide web is that the same infrastructures can be shared for moments important to “history” but also to the individual. It preserves images from cultural heritage institutions and family beach outings alike.

Flickr is three things at once: an archive and a dataset, most famously. But it is also a kind of data infrastructure. Let’s explore these one by one.

Flickr is an archive. It is a website that preserves history. It holds digital copies of historical artifacts for individual reflection and context. Flickr is a website for memories, stored in its copies of images, snapshots, aids to the remembrance of personal stories. These are assembled into an archive, a collective photo album. Flickr as an archive is a place where the context of an individual item is preserved. But we make sense of this archive socially. Meanings change as users sort these images, tag them, and reuse them (with permission) across the web. The archive is a collection of images with their own history beyond the website itself.

Flickr is a dataset. Flickr images can be described, at scale, in pure numbers. In 2011, the web site claimed to have 6 billion images and more recently boasted of having “tens of billions” of photos, with estimates of 25 million uploads per day. By contrast, the largest widely used image dataset used in machine learning, LAION 5B, contains roughly 5.85 billion images. Flickr as a massive, expanding dataset poses a particular set of challenges in thinking about its future. One of these is the daunting task of sorting and understanding all of those images. The dataset, then, is really just the archive viewed through the abstraction of scale. Billions of images now seen as one data set, with each image merely a piece of the collective whole. As a dataset, we focus on the ways the entirety of that set can be preserved and understood.

But in shifting our lens of focus from an archive to a dataset, individual details become less important. In changing scales in this way, it’s important to move fluidly between them — much as we close one eye, then the other, as we look at the letters of the eye exam. If we want to tackle the myopia of design decisions, we must get used to shifting between these two views, rather than treating one as the sole way we see the world.

What does it mean for Flickr to be “infrastructure” for AI? It helps to define this slippery term, so I turn to a definition used by the Initiative for Public Digital Infrastructure at UMass Amherst:

“Infrastructures are fundamental systems that allow us to build other systems—new houses and businesses rely on the infrastructures of electric power lines, water mains, and roads—and infrastructures are often invisible so long as they work well.”

In the relationship to images in particular, Katrina Sluis describes the shift in meaning attributed to images as their context shifts from archive to data infrastructures:

“Photographic culture is now being sustained by a variety of agents who sit outside the traditional scope of cultural institutions. I’m thinking here of the computer scientist, web designer, Silicon Valley entrepeneur or Amazon Mechanical Turker. And of course, these are actors who are not engaged with photographic culture and the politics of representation, the history of photography or the inherent polysemy of the image. In the computer science lab, the photograph remains relatively uncomplicated – it is ultimately a blob of information – whether materialized as a “picture” or left latent as data.”

Flickr’s images played an important role in shaping image recognition systems at the outset, and in turn, image generation systems. As a result of this entrenchment of images into AI, many Flickr images have become a form of “accidental infrastructure” for AI. I should be clear that Flickr has not trained a generative AI model of its own for the production of new images, nor has it arranged, as of this writing, for the sale of images for use in image training.

When we examine Flickr as infrastructure, we will see that these two worlds — archive and dataset — have come to occupy the same space, complicating our understanding of them both. Flickr’s movement from archive to dataset in the eyes of AI training isn’t permanent. It reflects a historical shift in the ways people understand and relate to images. So it is worth exploring how that shift changes what we see, and how ghosts from the archive come to “haunt” the dataset. In establishing these two lenses of focus, we might find strategies of shifting between the two. This can help us better articulate the context of images that have built, and likely will continue to build, the foundations of generative AI systems and the images these systems produce.

Flickers in the Infrastructure

How did Flickr’s transition from archive to dataset allow it to become a piece of AI infrastructure?

It started with one of the first breakthroughs in AI image generation — StyleGAN 2. StyleGAN 2 could produce images of human faces that were nearly photorealistic. It was entirely a result of the FFHQ dataset, which NVIDIA made from 70,000 Flickr portraits of faces. NVIDIA’s dataset drew on photographs from Flickr and, notably, warned that the dataset would inherit Flickr’s biases. The FFHQ dataset also went on to be used for image labeling and face recognition technologies, too.

We can easily trace the influence of that dataset on the faces StyleGAN 2 produced. In 2019, I did my own analysis of that dataset, looking image by image at the collection. In so doing, I examined the dataset through the lens of an archive. I looked at it as a collection of individual photographs, and individual people. I discovered that less than 3% of the faces sampled from the dataset contained black women. As a result, the faces produced by the image model were less likely to generate faces of black women. When it did, they were less photorealistic than other faces. The absences were shaping what we saw.

If datasets are haunted, then the synthetic image is a seance — a way of generating a specter from the datasets. This word, specter, refers to both the appearance of a spirit, but also the appearance of an image, deriving from the Latin for spectrum. The synthetic image is a specter. It’s an image which appears from an unknown place. It is one slice from a spectrum of possible images associated with a prompt. Absences in the dataset constrained the output of possible images. Without black women in the dataset, black women were not in the images. This is one way absences can haunt the synthetic image.

But there is another case study worth exploring, which is the ways that Flickr haunts infrastructures of AI. How did the dataset shape the automated decision making processes that were then included in longer, more complex systems of image generation?

In part two of this blog post, we’ll look at YFCC100M, a dataset of 99.2 million photos released in June 2014. And we’ll look at the path it has taken as it moved the world’s relationship to this collection of Flickr images from images, into an archive, into a dataset. Along the way, we’ll see how that dataset, by becoming a go-to reference for calibration and testing of image recognition and synthesis, became infused into the infrastructures of generated images.

Introducing Eryk Salvaggio, 2024 Research Fellow

Eryk Salvaggio is a researcher and new media artist interested in the social and cultural impacts of artificial intelligence. His work, which is centered in creative misuse and the right to refuse, critiques the mythologies and ideologies of tech design that ignore the gaps between datasets and the world they claim to represent. A blend of hacker, policy researcher, designer and artist, he has been published in academic journals, spoken at music and film festivals, and consulted on tech policy at the national level.

Ghosts in the Archives Become Ghosts in the Machines

I’m honored to be joining the Flickr Foundation to imagine  the next 100 years of Flickr, thinking critically about the relationships between datasets, history, and archives in the age of generative AI. 

AI is thick with stories, but we tend to only focus on one of them. The big AI story is that, with enough data and enough computing power, we might someday build a new caretaker for the human race: a so-called “superintelligence.” While this story drives human dreams and fears—and dominates the media sphere and policy imagination—it obscures the more realistic story about AI: what it is, what it means, and how it was built.

The invisible stories of AI are hidden in its training data. They are human: photographs of loved ones, favorite places, things meant to be looked at and shared. Some of them are tragic or traumatic. When we look at the output of a large language model (LLM), or the images made by a diffusion model, we’re seeing a reanimation of thousands of points of visual data — data that was generated by people like you and me, posting experiences and art to other people over the World Wide Web. It’s the story of our heritage, archives and the vast body of human visual culture. 

I approach generated images as a kind of seance, a reanimation of these archives and data points which serve as the techno-social debris of our past. These images are broken down — diffused — into new images by machine learning models. But what ghosts from the past move into the images these models make? What haunts the generated image from within the training data? 

In “Seance of the Digital Image” I began to seek out the “ghosts” that haunt the material that machines use to make new images. In my residency with the Flickr Foundation, I’ll continue to dig into training data — particularly, the Flickr Commons collection — to see the ways it shapes AI-generated images. These will not be one to one correlations, because that’s not how these models work.

So how do these diffusion models work? How do we make an image with AI? The answer to this question is often technical: a system of diffusion, in which training images are broken down into noise and reassembled. But this answer ignores the cultural component of the generated image. Generative AI is a product of training datasets scraped from the web, and entangled in these datasets are vast troves of cultural heritage data and photographic archives. When training data-driven AI tools, we are diffusing data, but we are also diffusing visual culture. 


Eryk Salvaggio: Flowers Blooming Backward Into Noise (2023) from ARRG! on Vimeo.


In my research, I have developed a methodology for “reading” AI-generated images as the products of these datasets, as a way of interrogating the biases that underwrite them. Since then, I have taken an interest in this way of reading for understanding the lineage, or genealogy, of generated images: what stew do these images make with our archives? Where does it learn the concept of what represents a person, or a tree, or even an archive? Again, we know the technical answer. But what is the cultural answer to this question? 

By looking at generated images and the prompts used to make them, we’ll build a way to map their lineages: the history that shapes and defines key concepts and words for image models. My hope is that this endeavor shows us new ways of looking at generated images, and to surface new stories about what such images mean.

As the tech industry continues building new infrastructures on this training data, our window of opportunity for deciding what we give away to these machines is closing, and understanding what is in those datasets is difficult, if not impossible. Much of the training data is proprietary, or has been taken offline. While we cannot map generated images to their true training data, massive online archives like Flickr give us insight into what they might be. Through my work with the Flickr Foundation, I’ll look at the images from institutions and users to think about what these images mean in this generated era. 

In this sense, I will interrogate what haunts a generated image, but also what haunts the original archives: what stories do we tell, and which do we lose? I hope to reverse the generated image in a meaningful way: to break the resulting image apart, tackling correlations between the datasets that train them, the archives that built those datasets, and the images that emerge from those entanglements.