What’s drawn you to family archives?

For a class on research-creation methods (also referred to as arts-based research) I took in 2019, I had big ambitions of creating an experimental, non-narrative documentary using cellphone footage during a planned trip to my parents’ home country of Malaysia. Upon returning home and examining the footage, I unfortunately came to the realization that a combination of obsolete technology (an iPhone 4S in the year of the iPhone 11 – imagine!!!), corrupted sound, and sabotage by my own, unsteady hands rendered my footage unusable. Scrambling to find some way to complete my term project in the final weeks of the semester, I decided to undergo what I saw as an intrapersonal reflection via an investigation of my own family archives. 

These “archives” are fairly small. Limited to the albums of photos my parents once carefully categorized and now just haphazardly store in a pile on a basement shelf. I confess that when living with my parents as a child, I was too self-centred to pay attention to any of the albums that didn’t include me. As the firstborn and only a year after my parents’ marriage, effectively I was prominently featured in all the albums except one. Paging through the album documenting my parents’ courtship, wedding, and first year of marriage, I was embarrassed by my shock of confronting these two people, whom I’ve evidently known my whole life, living this whole other life without me. As I passed on to the albums of my infancy, I became overcome with emotion seeing them making their life together, still virtually strangers having had an arranged marriage and finding themselves shortly thereafter in a new country, facing what I know now as the pressures that come with being not only new parents, but new immigrants, newly coupled, and struggling with finding lasting employment. 

Inspired by my reaction to these albums, I planned on conducting an oral history interview with my parents. I wanted to know the people in these photos, what they were thinking, feeling, doing. Yet there was something holding me back. The photos were so intimate, often only one of them in the shot, as the other was behind the camera. These felt like private moments between the two of them that was solely theirs to wholly know and experience. Instead, I took a selection of these photos and wrote my own reflections. Searching back through my own memories of the rare times my parents spoke about their youth and the early years of their marriage, I pieced together a history of their early settlement and parenthood in Canada (circa 1991-1995) through written reflections and image descriptions I then inscribed on the digitized copies of the photos. I’m usually not a very emotionally expressive person, but I cried when I presented this to my class. 

This experience fundamentally changed my relationship to photo archives and sowed the seeds for what would become my master’s thesis South Asian Instagram Community Archives: A Platform for Performance, Curation, and Identity as well as my approach to creative and poetic visual description for blind and low-vision communities as workshopped in the online exhibitions Audio Description in the Making and Air, River, Sea, Soil: A History of an Exploited Land. 

Continuing this line of engagement along with my community archival engagement approaches prototyped in the community digital archive/exhibition project Things+Time,

What would you like to work on during your fellowship?

I will, over the course of my fellowship at the Flickr Foundation, work with two community organizations, one based in London, UK and one in Montreal, Canada to undergo a digital archival excavation workshop. Through a series of guided prompts and reflections, these community groups will decide specific search criteria in order to activate the Flickr archive, creating informal collections that respond to and inform the earlier reflections. Together, community members will create descriptions for the images that can dually serve as archival and visual descriptions for potential use in a future exhibition.

Using a “photovoice” methodology, participants will also be tasked with adding their own, related and annotated material to the Flickr archive in response to the collective and reflections feedback from the workshop.

The goals of this project are to engage community organizations with the creative possibilities afforded through archival and photo research as well as to unearth and activate some of the rich histories embedded in the Flickr archive.

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.