Spencer Baird, the American naturalist and first curator of the Smithsonian Institution, instructed his collectors in ‘the field’ what to collect, how to describe it and how to preserve it until returning back Eastwards, carts laden. His directions included:

Birds and mammalia larger than a rat should be skinned. For insects and bugs — the harder kinds may be put in liquor, but the vessels and bottles should not be very large. Fishes under six inches in length need not have the abdominal incision… Specimens with scales and fins perfect, should be selected and if convenient, stitched or pinned in bits of muslin to preserve the scales. Skulls of quadrupeds may be prepared by boiling in water for a few hours… A little potash or ley will facilitate the operation.

Baird’s 1848 General Directions for Collecting and Preserving Objects of Natural History is an example of a collecting guide, also known at the time as a desiderata (literally ‘desired things’). It is this archival architecture that Hannah Turner (2021) takes critical aim at in Cataloguing Culture: Legacies of Colonialism in Museum Documentation. According to Turner, Baird’s design “enabled collectors in the field and museum workers to slot objects into existing categories of knowledge”.

Whilst the desiderata prompted the diffuse and amateur spread of collecting in the 19th century, no doubt flooding burgeoning institutional collections with artefacts from the so-called ‘field’, the input and classification systems these collecting guides held came with their own risks. Baird’s 1848 desiderata shockingly includes human subjects—Indigenous people—perceived as extensions of the natural world and thus procurable materials in a concerted attempt to both Other and historicise. Later collecting guides would be issued for indigenous tribal artefacts, such as the Haíłzaqv-Haida Great Canoe – now in the American Museum of Natural History’s Northwest Coast Hall – as well as capturing intangible cultural artefacts – as documented in Kara Lewis’ study of the 1890 collection of Passamaquoddy wax recording cylinders used for tribal music and language. But Turner pivots our focus away from what has been collected, and instead towards how these objects were collected, explaining, “practices and technologies, embedded in catalogues, have ethical consequences”.

While many physical artefacts have been returned to Indigenous tribes through activist-turned-institutional measures (such as the repatriation of Iroquois Wampum belts from the National Museum of the American Indian or the Bååstede project returning Sami cultural heritage from Norway’s national museums), the logic of the collecting guides remains. Two centuries later, the nomenclature and classification systems from these collecting guides have been largely transposed into digital collection management systems (CMS), along with digital copies of the objects themselves. Despite noteworthy efforts to to provide greater access and transparency through F.A.I.R. principles or rewrite and reclaim archival knowledge systems—such as Traditional Knowledge (T.K.) Labels and C.A.R.E. principles, Kara Lewis (2024) notes that “because these systems developed out of the classification structures before them, and regardless of how much more open and accessible they become, they continue to live with the colonial legacies ingrained within them”. The slowness of the Galleries, Libraries, Archives and Museums (G.L.A.M.) sector to adapt, Lewis continues, stems less from “an unwillingness to change, and more with budgets that do not prioritize CMS customizations”. Evidently a challenge lies in the rigidly programmed nature of rationalising cultural description for computational input.

In our own Content Mobility programme, the Data Lifeboat project, we propose that creators write a README. In our working prototype, the input is an open-text field, allowing creators to write as much or as little as they wish about their Data Lifeboat’s purpose, contents, and future intentions. However, considering Turner’s cautionary perspective, we face a modern parallel: today’s desiderata is data, and the field is the social web—deceptively public for users to browse and “Right-Click-Save” at will. We realised that in designing the input architecture for Data Lifeboats, we could inadvertently be creating a 21st century desiderata: a seemingly open and neutral digital collecting tool that beneath the surface risks perpetuating existing inequalities.

This blog-post will introduce the theoretical and ethical underpinnings to the Data Lifeboat’s collecting guide, or README, that we want to design. The decades of remedy and reconciliatory work, tirelessly driven primarily by Indigenous rights activists, in addressing the archival injustices first cemented by early collecting guides provides a robust starting point for embedding ethics into the Data Lifeboat. Indigenous cultural heritage inevitably exists within Flickr’s collections, particularly among our Flickr Commons members who are actively pursuing their own reconciliation initiatives. Yet the value of these interventions extends beyond Indigenous cultural heritage, serving as a foundation for ethical data practices that benefit all data subjects in the age of Big Data.

A Brief History of C.A.R.E Principles

Building on decades of Indigenous activism and scholarship in restitution and reconciliation, the C.A.R.E. principles emerged in 2018 from a robust lineage of interventions, such as Native American Graves Protection and Repatriation Act (NAGPRA, 1990) and The United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP, 2007), which sought to recognise and restore Indigenous sovereignty over tangible and intangible cultural heritage.

These earlier frameworks were primarily rooted in consultation processes with Indigenous communities, ensuring that their consent and governance shaped the management of artefacts and knowledge systems. For instance, NAGPRA enabled tribes to reclaim human remains and sacred objects through formalised dialogues and consultation sessions with museums. Similarly, Traditional Knowledge Labels (Local Contexts Initiative) were designed to identify Indigenous protocols for accessing and using knowledge within the museum’s existing collection, for instance a tribal object may be reserved for viewing only by female tribal members. These methods worked effectively within the domain of physical collections but faltered when confronted with the scale and opaqueness of data in the digital age.

In this context, Indigenous governance of data emerged as essential, particularly for sensitive datasets such as health records, where documented misuse showed evidence of perpetuating harm. As the Data Science field developed, it often prioritised the technical ideals of F.A.I.R. principles (Findable, Accessible, Interoperable, Reusable), which advocate for improved usability and discoverability of data, to counter increasingly oblique and privatised resources. Though valuable, F.A.I.R. principles fell short on the ethical dimensions of data, particularly on how data is collected and used in ways that affect already at-risk communities (see also O’Neil 2016, Eubanks 2018, and Benjamin 2019). As the Global Indigenous Data Alliance argued:

“Mainstream values related to research and data are often inconsistent with Indigenous cultures and collective rights”

Recognising the challenges posed by Big Data and Machine Learning (ML)—from entrenched bias in data to the opacity of ML algorithms—Indigenous groups such as the Te Mana Raraunga Māori Data Sovereignty Network, the US Indigenous Data Sovereignty Network, and the Maiam nayri Wingara Aboriginal and Torres Strait Islander Data Sovereignty Collective led efforts to articulate frameworks for ethical data governance. These efforts culminated in a global, inter-tribal workshop in Gaborone, Botswana, in 2018, convened by Stephanie Russo Carroll and Maui Hudson in collaboration with the Research Data Alliance (RDA) International Indigenous Data Sovereignty Interest Group. The workshop formalised the C.A.R.E. principles, which were published by the Global Indigenous Data Alliance in September 2019 and proposed as a governance framework with people and purpose at its core.

The C.A.R.E. principles foreground the following four values around data:

1. Collective Benefit: Data must enhance collective well-being and serve the communities to which it pertains.
2. Authority to Control: Communities must retain governance over their data and decide how it is accessed, used, and shared.
3. Responsibility: Data handlers must minimise harm and ensure alignment with community values.
4. Ethics: Ethical considerations rooted in cultural values and collective rights must guide all stages of the data lifecycle.

C.A.R.E. in Data Lifeboats?

While the C.A.R.E. principles were initially developed to address historical data inequities and exploitation faced by Indigenous communities, they offer a framework that can benefit all data practices: as the Global Indigenous Data Alliance argues, “Being CARE-Full is a prerequisite for equitable data and data practices.”

We believe the principles are important for Data Lifeboat, as collecting networked images from Flickr poses the following complexities:

• Data Lifeboat creators will be able to include images from Flickr Commons members (which may include images of culturally sensitive content)
• Data Lifeboat creators may be able to include images from other Flickr members, besides themselves
• Subjects of photographs in a Data Lifeboat may be from historically at-risk groups
• Data Lifeboats are designed to last and therefore may be separated from their original owners, intents and contexts.

The Global Inidgenous Data Alliance asserts, their principles must guide every stage of data governance “from collection to curation, from access to application, with implications and responsibilities for a broad range of entities from funders to data users.” The creation of a Data Lifeboat is an opportunity to create a new collection, thus we have the opportunity to embed C.A.R.E. principles from the start. Although we cannot control how Data Lifeboats will be used or handled after their creation, we can attempt to establish an architecture for encouraging that C.A.R.E. is deployed throughout the data lifecycle.

Enter: The README

Our ambition for the Data Lifeboat (and the ethos behind many of Flickr.org programmes) is the principle of “conscious collecting”. We aim to move away from the mindset of perpetual accumulation that plagues both museums and Big Tech alike—a mindset that advances a dangerous future, as cautioned by both anti-colonialist and environmentalist critiques. Conscious collecting allows us to better consider and care for what we already have.

One of the possible ways we can embed conscious collecting is through the inclusion of a README—a reflective, narrative-driven process for creating a Data Lifeboat.

READMEs are files traditionally used in software development and distribution that contain information about files within the directory. It is often in the form of plain text (.txt, .md), to maximise readability, frequently containing information about operating instructions, troubleshooting, credits, licensing and changelogs, intended to be read on start-up. In the Data Lifeboat, we have adopted this container to supplement the files. Data Lifeboat creators are introduced to the README in the creation process and, in the present prototype, are met with the following prompts to assist writing:

• Tell the future why you are making this Data Lifeboat.
• Is there anything special you’d like future viewers to know about the contents? Anything to be careful about?

(These prompts are not fixed, as you’ll read in Part 2)

During our workshops, participants noted the positive (and rarely seen) experience of introducing friction to data preservation. This friction slows down the act of collecting and creates space to engage with the social and ethical dimensions of the content. As Christen & Andersen (2019) emphasise in their call for Slow Archives, “Slowing down creates a necessary space for emphasising how knowledge is produced, circulated, and exchanged through a series of relationships”. We hope that Data Lifeboat’s README will contribute to Christen & Andersen’s invocation for the “development of new methodologies that move toward archival justice that is reparative, reflective, accountable, and restorative”.

We propose three primary functions of the README in a Data Lifeboat:

1. Telling the Story of an Archive

   Boast, Bravo, and Srinivasan (2018), reflecting on Inuit artefacts in an institutional museum collection, write that its transplant results in the deprivation of “richly situated life of objects in their communities and places of origin.” Once subsumed into a collection, artefacts often suffer the “loss of narrative and thick descriptions when transporting them to distant collections”.

   We are conscious that this could be the fate of many images once transplanted in a Data Lifeboat. Questions emerged in our workshops as to how to maintain the contextual world around the object, speaking of not only its social metadata (comments, tags, groups, albums) but also the more personal levers of choice, value and connection. A README resists the diminishment of narrative by creating opportunities to retain and reflect on the relational life of the materials.

   The README directly resists the archival instinct toward neutrality, by its very format it holds that this can never be true. Boden critiques the paucity of current content management systems, their highly structured input formats cannot meet our responsibilities to communities as they do not give space to fully citing how information came to be known and associated with an object and on whose authority. Boden argues for “reflections on the knowledge production process”, which is what we intend the README to encourage the Data Lifeboat creator to do. The README prompts (could) suggest Data Lifeboat creator reflect on issues around ownership (e.g. is this your photo?), consent (e.g. were all photo subjects able to consent to inclusion in a Data Lifeboat?), and embedded power relations (e.g. are there any persecuted minorities in this Data Lifeboat?): acknowledging the archive is never objective.

   More poetically, the README could prompt greater storytelling, serving as a canvas for both critical and emotional reflection on the content of a Data Lifeboat. Through guided prompts, creators could explore their personal connections to the images, share the stories behind their selection process, and document the emotional resonance of their collection. A README allows creators to capture and contextualise not only the images themselves, but to add layers of personal inscription and meaning, creating a richer, more distributed archive.

2. Decentralised and Distributed Annotation

   The Data Lifeboat constitutes a new collecting format that intends to operate outside traditional archival systems’ rigid limitations and universalising classification schemes. The README encourages decentralised curation and annotation by enabling communities to directly contribute to selecting and contextualising archival and contemporary images, fostering what Huvila (2008) terms the ‘participatory archive’ [more on Data Lifeboat as a tool for decentralised curation here].

   User-generated descriptions such as comments, tags, groups, and galleries — known on Flickr as ‘social metadata’ —serve as “ontological keys that unlock the doors to diverse, rich, and incommensurable knowledge communities” (Boast et al., 2018), embracing multiple ways of knowing the world. Together, these create ‘folksonomies’—socially-generated digital classification systems that David Sturz argues are particularly well-suited to “loosely-defined, developing fields,” such as photo subjects and themes often overlooked by the institutional canon. The Data Lifeboat captures the rich, social media that is native to Flickr, preserving decades worth of user contributions.

   The success of community annotation projects has been well-documented. The Library of Congress’s own Flickr Pilot Project demonstrated how community input enhanced detail, correction, and enrichment. As Michelle Springer et al. (2018) note, “many of our old photos came to us with very little description and that additional description would be appreciated”. Within nine months of joining Flickr, committing to a hands-off approach, the Library of Congress accumulated 67,000 community-added tags. “The wealth of interaction and engagement that has taken place within the comments section has resulted in immediate benefits both for the Library and users of the collections,” continues Springer et al. After staff verification, these corrections and additions to captions and titles demonstrated how decentralised annotation could reshape the central archive itself. As Laura Farley (2014) observes, community annotation “challenges archivists to see their collections not as closely guarded property of the repository, but instead as records belonging to a society of users”.

   Beyond capturing existing metadata, the README enables Data Lifeboat creators to add free-form context, such as correcting erroneous tags or clarifying specific terminology that future viewers might misinterpret—like the Portals to Hell group. As Duff and Harris (2002) write, “the power to describe is the power to make and remake records and to determine how they will be used and remade in the future. Each story we tell about our records, each description we compile, changes the meaning of records and recreates them” — the README hands over the narrative power to describe.

3. Data Restitution and Justice

   Thinking speculatively, the README could serve an even more transformative purpose as a tool for digital restitution. Through the Data Lifeboat framework, communities could reclaim contested archival materials and reintegrate them into their own digital ecosystems. This approach aligns with “Steal It Back” (Rivera-Carlisle, 2023) initiatives such as Looty, which creates digital twins of contested artefacts, currently held in Western museums. By leveraging digital technologies, these initiatives counter the slow response of GLAM institutions to restitution calls. As Pavis and Wallace (2023) note, digital restitution offers the chance to “reverse existing power hierarchies and restore power with the peoples, communities, and countries of origin”. In essence, this offers a form of “platform exit” that carves an alternative avenue of control of content to original creators or communities, regardless of who initially uploaded the materials. In an age of encroaching data extractivism, the power to disengage, though severe, for at-risk communities can be the “reassertion of autonomy and agency in the face of pervasive connectivity” (Kaun and Treré, 2021).

   It is a well-documented challenge in digital archives that many of the original uploaders were not the original creators, which prompts ought to prompt reflections around copyright and privacy. As Payal Arora (2019) has noted our dominant frameworks largely ignore empirical realities of the Global South: “We need to open our purview to alternative meanings including paying heed to the desire for selective visibility, how privacy is often not a choice, and how the cost of privacy is deeply subjective”. Within the README, Data Lifeboat creators can establish terms for their collections, specifying viewing contexts, usage conditions, and other critical contextual information. They can also specify restrictions on where and how their images may be hosted or reused in the future (e.g. ‘I refuse to let these image be used in AI training data sets’). A README could allow for Data Lifeboat creators to expand and detail more fluid and cultural and context-specific conditions for privacy and re-use.

   At best, these terms would allow Data Lifeboat creators to articulate their preferences for how their materials are accessed, interpreted and reused in the future, functioning as an ethical safeguard. While these terms may not always be enforceable, they provide a clear record of the creators’ intentions. Looking ahead, we could envision the possibility of making these terms machine-readable and executable. The sustenance of these terms could potentially be incorporated into the governance framework of the Safe Harbor Network, our proposed decentralised storage system of cultural institutions that can hold Data Lifeboats for the long-term.

Discussion: README as a Datasheet for Networked Social Photography Data Sets?

In the long history of cataloging and annotating data, Timnit Gebru et al.’s (2018) Datasheets for Datasets stands out as an emerging best practice for the machine learning age. These datasheets provide “a structured approach to the description of datasets,” documenting provenance, purpose, and ethical considerations. By encouraging creators to critically reflect on the collection, composition, and application of datasets, datasheets foster transparency and accountability in an otherwise vast, opaque, and voraciously consuming sphere.

The Digital Cultural Heritage space has made similar calls for datasheets in archival contexts, as they too handle large volumes of often uncontextualised and culturally sensitive data. As Alkemade et al. (2023) note, cultural heritage data is unique: “They are extremely diverse by nature, biased by definition and hardly ever created or collected with computation in mind”. They argue, “In contrast to industrial or research datasets that are assembled to create knowledge… cultural heritage datasets may present knowledge as it was fabricated in earlier times, or community-based knowledge from lost local contexts”. Given this uniqueness, digital cultural heritage requires a tailored datasheet format that enables rich, detailed contextualization reflecting both the passage of time and potentially lost or inaccessible meanings. Just as datasheets have transformed technical datasets, the README has the potential to reshape how we collect, interpret, and preserve the networked social photography that is native to the Flickr.com platform — something we argue is part of our collective digital heritage.

There are, of course, limitations—neither datasheets nor READMEs will be a panacea for C.A.R.E-full data practices. Gebru et al. acknowledge that “Dataset creators cannot anticipate every possible use of a database”. The descriptive approach also presents possible trade-offs: “identifying unwanted societal biases often requires additional labels indicating demographic information about individuals,” which may conflict with privacy or data protection. Gebru notes that the Datasheet “will necessarily impose overhead on dataset creator”—we recognise this friction as a positive. Echoing Christen and Anderson’s call “Slowing down is about focusing differently, listening carefully, and acting ethically“.

Conclusion

Our hope is that the README is both a reflective and instructive tool that prompts Data Lifeboat Creators to consider the needs and wishes of each of the four main user groups in the Data Lifeboat ecosystem:

1. Flickr Members
2. Data Lifeboats Creators
3. Safe Harbor Dock Operators
4. Subjects in the Photo

While we do not yet know precisely what form the README will take, we hope our iterative design process can offer flexibility to accommodate the needs of—and our responsibilities to—Data Lifeboat creators, photographic subjects and communities, and future viewers.

In our Mellon-funded Data Lifeboat workshops in October and November, we asked our participants to support us in co-designing a digital collecting tool with care in mind. We asked:

What prompts or questions for Data Lifeboat creators could we include in the README to help them think about C.A.R.E. or F.A.I.R. principles. Try to map each question to a letter.

The results of this exercise and what this means for Data Lifeboat development will be detailed in Part 2.

 

The photographs in this blog post come from the Smithsonian Institution’s Thomas Smillie Collection (Record Unit 95) – Thomas Smillie served as the first official photographer for the Smithsonian Institution from 1870 until his death in 1917. As head of the photography lab as well as its curator, he was responsible for photographing all of the exhibits, objects, and expeditions, leaving an informal record of early Smithsonian collections.

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On Saturday, December 7th 2024, Notre Dame Cathedral reopened its iron-clad tracery doors, marking the end of a four-year closure. The news coverage focused on the splendour — and occasional controversy — of its distinguished guests, contentious seating plans and retro light shows. The reopening inevitably brought back memories of the 2019 tragedy that befell the cathedral, destroyed by fire. Somehow the event underscored our collective helplessness under the Covid-19 lockdowns as viewers could only watch in horror as the same images spread around news and social media. On reflection the ubiquity and uniformity of the images is surprising, so often captured from the southeast end of the chancel: the flechè engulfed in flames like a tapered candle, and behind it through the iridescent smoke, a pair of lancet windows seemed to peer back at the viewer, embodying a vision of Notre Dame des Larmes—Our Lady in tears.

There is an alternative history of the Notre Dame fire, captured not through mainstream media but in the dispersed archives of networked social photography—an often overlooked and underreported lens on the event. Among the pictures gathered by the Flickr Foundation is a remarkable collection of street photographs that offer a fresh perspective (also shared in this post). These images place the fire in context: smoke billows against the backdrop of the Eiffel Tower as seen from Trocadéro; a woman in a fur coat strolls nonchalantly past the boarded-up bouquinistes; a couple steal a glance from their moped; a man abandons his bicycle, supplicating on the banks of the Seine. User-generated social photography expands the event from its mass-reproduced, singular, fixed perspective, into a multi-dimensional, multi-vocal narrative, that unfolds longitudinally over time.

This is the incantation of social photography at its best, so often dismissed for its sheer volume, producing images that are “unoriginal, obvious, boring.” Yet, as art historian Geoffrey Batchen counters, “There are no such things as banal photographs, only banal accounts of photography.” The true value lies not just in the images themselves, but in how we look at them. It is through this act of curation, contextualisation and interpretation that these photographs gain their depth.

 

A People’s History through the Lens

Embedded within the story of photography itself is a people’s history. From its inception, photography has centred the social subject, capturing the overlooked and hidden realities that traditional media refused to. In mid-19th century Paris, early photographers, Eugène Atget and Félix Nadar chronicled the changing urban landscapes, preserving scenes of working-class neighbourhoods, subject to Haussman’s destruction, proletarian characters and everyday life. The camera’s portability, speed and (perceived) candidness made it suitable to the task of documenting the unseen.

The social subject in photography has long been intended to elicit sentiment and action. Social photographs compel viewers to respond emotionally and, ideally, to take action. Jacob Riis’s How the Other Half Lives (1888), aimed at middle-class audiences, used images of New York’s Lower East Side slums to generate empathy and drive charity. As Walter Benjamin later described it, the medium of photography has a “revolutionary use-value” in its ability to render visible hidden social structures. Contemporary documentary collections, such Kris Graves’s A Bleak Reality, have harnessed this historic compulsion to catalyse social change.

There is rightly so, caution against social photography’s treatment of its subjects. As Maren Stange argues, in her analysis of American documentary photographers including Riis (along with Hine and the famed Farm Security Administration collection), social photography has historically rested on assumptions about its subjects, instrumentalising them or reducing them to symbolic devices. Moreover, it often fails to acknowledge that the photographer inherently constructs the photograph’s reality. As David Peeler notes, each photograph holds “layers of accreted meanings” shaped by the photographer’s choice in composition, processing, and presentation. In the age of citizen-driven photography, with the distributed ability to manipulate images, these limitations become even more pronounced, requiring an explicit recognition of the constructed nature of the medium.

The construction of this fragment of reality in photography, however, is not inherently negative. When acknowledged, it can be a source of power, offering what Donna Haraway describes as situated knowledge—a rejection of objectivity in favor of partial, contextual perspectives. Citizen-driven collections, though subjective and, by their very nature incomplete, serve as an antidote to what Haraway calls the “conquering gaze from nowhere.” They counter dominant, institutional narratives with fragmented, personal views.

 

The Photograph Over Time

The value of a photograph often increases over time, as its meaning and significance evolve in relation to historical, cultural, and social contexts. Photographs gain depth through longitudinal perspectives, becoming more compelling at a distance as they reveal shifts, absences, and forgotten details. As Silke Helmerdig, in Fragments, Futures, Absence and the Past, discusses in her treatment of German photography of the 2010s, we ought to see photography as speaking to us in the future subjunctive, it asks us, “what could be, if?”

With time and attention, photographs can be viewed in aggregate, the future historian can pull from concurrent sources. Our contemporary photographic collecting tools, as in the case of Flickr’s Galleries and Albums, which allow curation of others people’s photographs, can come to resemble a sort of photomontage. Rosalind Krauss, writing on the photomontages of Hannah Höch and other Dadaists in The Optical Unconscious, argues that the medium forces a dialogue between images, creating unexpected connections and breaking the linearity of traditional visual narratives thus opening space for political critique. The Notre Dame gallery disrupts the throughline of the ‘official’ imagery of the event, creating a space for discourse of other elements besides the central action (e.g. gender, capitalism, urbanism).

Securing the People’s Archive

Having discussed the value of the citizen-made collection, this compels us to ask what if our institutional archives began collecting contemporaneously more? We believe Data Lifeboat can help with this. The Notre Dame Gallery is just one example of a potential collection for a Data Lifeboat: our tool for long-term preservation of networked social photography and citizen-made collections from Flickr.com.

Data Lifeboats could be deployed as a curatorial tool for networked social photography, providing institutions with a way to collect, catalogue and reflect on citizen-driven narratives. At present, there is not an archival standard for images on social media and archivists still struggle with the vastness and maintenance of those they’ve managed to collect [see our blog-post from iPres]. Data Lifeboat thus operates as a sort of packaging tool, flexible and light enough to adapt to collections of differing scales and purviews, but still maintaining the social context that makes networked images so valuable.

There are two potential approaches:

1. Hands-on: Data Lifeboats could be commissioned by an institution around a certain topic. For example, the Museum of London could commission a group of Whitechapel teenagers to collect photos from Flickr.com of their neighbourhood spaces that are meaningful to them.
2. Hands-off: Citizens create Data Lifeboats independently of a topic of their choosing. Institutions may choose to hold these bounded social media archives as a public good, for the benefit of our collective digital heritage.

In both cases, the institutions become holders of Data Lifeboats and they are subsumed into their digital collections management systems. Data Lifeboats become part of a process of Participatory Appraisal, extending and diversifying the ‘official archive’, addressing the persistent gap of who gets to be represented. As we have also discussed, there are also possibilities for distributing the load of Data Lifeboats, more on this in the Safe Harbor Network.

Other possible Data Lifeboats

During our Mellon-funded workshops, we asked participants to suggest Data Lifeboats they would like to see in their institutional collections, but also any they would create themselves for personal use.

At-risk Subjects

Collections focus on documenting vulnerable or ephemeral content that might disappear without active intervention. This includes both environmental changes and socio-political documentation that could be censored or lost.

e.g. glaciers over time, rapid response after a disaster, disappearing rural life across Europe, politically at-risk accounts

Subjects often overlooked

Collections that aim to preserve marginalised voices and underrepresented perspectives, helping to fill gaps in traditional institutional archives and ensure a more representative historical record.

e.g. a queer community coffee shop, Black astronauts, local street art, life in Communist Poland

Nostalgia for Web 1.0

As so much of Web 1.0 disappears (e.g. Geocities, MySpace music, see also ‘Digital Dark Age‘), there is a desire to archive and begin critically reflecting on the early days of the web.

e.g. self-portraits from the early 2000s, vernacular photography from the 2010s, Flickr HQ, most viewed Flickr photos

Quirky Collections

Flickr is renowned as a home for serendipitous discovery on the web, sometimes lauded as ‘digital shoebox of photographs’, there is the opportunity to replicate this ‘quirkiness’ with Data Lifeboats.

e.g. ghost signs, every Po’Boy in town, electricity pylons of the world

Personal collections

e.g. family archives, 365 challenges, a group of friends

Data Lifeboats could serve as secure containers for digital family heirlooms. Built into Flickr.com are privacy controls (Friends, Family) that would carry over to Data Lifeboats, preserving privacy for the long-term

 

Conclusion

The Notre Dame gallery exemplifies an ideal subject for a Data Lifeboat, both in its content and curatorial approach. The Data Lifeboat framework serves as an apt vessel, with its built-in capabilities:

• Data Lifeboats can capture alternative viewpoints, situated knowledges and stories from below through tapping into the vast Flickr archive. We recognise that we can never capture, nor preserve, the archive in its entirety, so Data Lifeboats tap into the logic of the archival sliver.
• Data Lifeboats can preserve citizen-driven communication through their unique storage of social metadata. This means that the conversations around the images are preserved with the images themselves, creating a holistic entity.
• Data Lifeboats are purposely designed with posterity in mind. Technologically, their light-touch design means they are built to last. Furthermore, the README (link) nudges the Data Lifeboat creator toward conscious curation and commentary, providing value to future historians.

Can you think of any other Data Lifeboats? We’d love to hear about them.

As you may know, we had laid out doing two workshops:

1. Washington DC, at The Library of Congress, in October, and
2. London, at the Garden Museum and Autograph Gallery, in November.

We were pleased to welcome a total of 32 people across the events, from libraries, archives, academic institutions, the freelance world, other like-minded nonprofits, Flickr.com, and Flickr.org.

Now we are doing the work of sifting through the bazillion post-its and absorbing the great conversations had as we worked through Tori’s fantastic program for the event. We were all very well-fed and organized too, thanks to Ewa’s superb project management. Thank you both.

Workshop aims

The aims of each workshop were the same:

• Articulate the value of archiving social media, and Data Lifeboat
• Detail where Data Lifeboat fits in current ecology of tools and practices
• Detail where Data Lifeboat fits with curatorial approaches and content delivery
• Plot (and recognise) the type and amount of work it would take to establish Data Lifeboat or similar in organisations

Workshop outline

We met these aims by lining up the workshops into different sessions:

1. Foundations of Long-Term Digital Preservation – Backward/forward horizons; understanding digital infrastructures; work happening in long-term digital preservation
2. Data Lifeboat: What we’re thinking so far – Reporting on our NEH work to prototype software and policy, including a live demo; positioning a Data Lifeboat in emergency/not-emergency scenarios; curation needs or desires to use Data Lifeboats as selection/acquisition tool
3. Consent and Care in Social Media Archiving – Ethics of care in digital archives; social context and care vs extractive data practices; mapping ethical rights, risks, responsibilities including copyright and data protection, and consent, and
4. Characteristics of a Robust & Responsible Safe Harbor Network (our planned extension of the Data Lifeboat concept – think LOCKSS-ish) – The long history of safe harbor networks; logistics of such a network; Trust.

I’m not going to report on these now, but whet your appetite for our further reporting back.

Background readings

Tori also prepared some grounding readings for the event, which we thought others may like to review:

• The Archival Sliver: A Perspective on the Construction of Social Memory in Archives and the Transition from Apartheid to Democracy by Verne Harris
• Energy, Digital Preservation, and the Climate: Proactively Planning for an Uncertain Future by Sibyl Schaefer
• The Image As Witness: Collecting Visual Materials from the National Tragedy by Jeremy Adamson
• Digital Preservation: A Critical Vocabulary, Chapter 5: Risk by Rebecca Frank
• “I Can’t Wait for You to Die” A Community Archives Critique by Harrison Apple

Needless to say, we all enjoyed it very much, and heard the same from our attendees. Several follow-on chats have been arranged, and the community continues to wiggle towards each other.

Last week we passed a big milestone set out in the Strategy 2021-2023 – Flickr Commons Revitalization I wrote in 2021. As the strategy says, when the Commons launched in 2008, the program had two main objectives:

1. To increase public access to archival photography collections, and
2. To provide a way for the general public to contribute information and knowledge.

Our current work to reinvigorate the program introduces two new ones: 

1. To propagate updates from and to member catalogs and other sources, and
2. To protect and attend to the long life of this unique collection.

Even though it’s now 2024, we are still on the track we set out back in 2021. After doing research with Commons members to inform the strategy, we heard five main requests:

1. Add new Discovery layer and encourage contribution – We launched commons.flickr.org earlier in the year, and have continued to develop it with recent activity, an overview of conversations about the pictures, and a simple map. Alex made a changelog too, so you can see how it evolves.
2. Co-design granular, comparative, exportable stats – The company launched an enhancement to the core stats feature on Flickr.com November last year, which got most of the way to helping Flickr Commons members report to their people on how people are interacting with their accounts. Being able to justify your time to work on Flickr.com and community engagement inside museums, libraries, and archives is supported by this directly.
3. Improve description tools for regular researchers – We haven’t started on this in earnest yet, and hope to in 2025. (Would you like to be in our user group for this? Please get in touch.)
4. Incorporate CC0 and Public Domain Mark (PDM) – This is not done yet, but we have been advocating for the upgrade of CC 2.0 to CC 4.0 which is now a work in progress. We have also created a Collections Development Policy and other supporting content to help guide new members on what ‘no known copyright restrictions’ means and how to use it.
5. Streamline onboarding to easily manage members and participation – We are excited to be working with the company on developing new Commons-specific APIs to allow our team to build out new administration tools for the program. This will continue in 2025, and make everything much easier!

New members!

For the first time in several years, we’ve welcomed three new Commons members to the fold:

• Community Archives of Belleville & Hastings County
• Letterform Archive
• Port Morien Digital Archive

And we have a tranche of new members in the wings, ready to go, including our first member from India!

Seventeen years!

Flickr Commons was launched in 2008, so will be turning 17 in January. It was lovely for some of our team to visit with Michelle, Helena, Phil, and others in The Library of Congress Commons team last week. We were there for a Data Lifeboat workshop, and it was great to dream about what the next sixteen years could be like together! (We’ll be writing the workshops up separately.)

So, keep an eye out for more new members coming aboard, and if you work inside a cultural organization with a photography collection, get in touch to see if joining Flickr Commons could help your organization grow a new audience. (Short answer: it can!)

What is an archive?

At its simplest, an archive is a repository of historical records like photographs, documents, sound recordings, books and artworks. Speciality archives may focus on a particular medium, such as the Moving Image Archive or a place, like the London Metropolitan Archives. Archives house physical or digital records or a combination of both. Many archives are found within larger institutions such as universities, libraries, museums, government offices, and established public or private organizations. Usually, these archives have their materials grouped into collections managed by professionals called archivists. 

There are all kinds of informal archives as well. Lots of smaller community and cultural organizations keep records of their activities but may not have a dedicated archivist to keep them organized. We, individuals, also record our lives through photography, sometimes printing them or keeping them in digital photo albums, or online on various social media platforms like Instagram, Facebook, or Flickr.

What is Photovoice?

Originally conceived and put into practice by health researchers Caroline Wang and Mary Ann Burris in the early 1990s, Photovoice involves working alongside participants to take photographs and subsequently discuss them in order to be able to collectively illuminate and reflect upon contemporary issues within a community. At the end of the project, a selection of the photos taken and discussed is exhibited for the community to share the insights that were collectively produced. Often, researchers engaging in Photovoice seek to recalibrate the power imbalance between researcher and subject by lending the tools for research (i.e. the camera) to the active participants, thus elevating them to the position of collaborator, co-researcher, or co-producer.

Archivevoice is an extension of Photovoice, alongside others like Videovoice and Comicvoice. By using the principles of participatory action research developed in Photovoice, other researchers have modified their methods engaging in different artistic mediums for participants’ self-expression. Videovoice has the goal of getting “people, who are usually the subjects or consumers of mainstream media [to] get behind video cameras to research issues of concern, communicate their knowledge, and advocate for change.” Comicvoice, coined by John Baird, engages research groups in creating their own narratives from outsourced comics.

Why Archives?

There is so much rich, historical information available within archives. One of the limits of the Photovoice method is what is available to be photographed. Through Archivevoice, the research participants are able to navigate through much broader windows of time and space to reflect and discuss how historical events shape the contemporary moment. This kind of embodied practice of looking back and critically engaging with one’s community and culture through such a deep, reflective practice has been referred to by scholars as “ancestor work”. 

Archivevoice

Archivevoice is a Participatory Action Research (PAR) method that adapts the core principles of Photovoice using photographs or other archival documentation to undergo community-centered research.

“Through exhibitions and outreach events community members can be brought to the archive and made aware of what records are present in the archives. They can see themselves, their families, and their histories represented in the materials and engage with those who are responsible for preserving and describing that history. Through workshops and naming events, the archives can be brought to community members for the purpose of facilitating discussion, memory making, and healing together.”

– Kristen Young

What is the purpose of Archivevoice?

• To use photographs and other archival documentation to reflect on collective experiences affecting communities
• To gain insight about a particular community’s histories, activities, and concerns
• To engage communities with their own archival records
• To empower communities to lend their voice to heritage projects and document their own histories
• To have community participants be co-producers of research
• To activate the archive through creative presentation of the selected records (e.g. exhibition, zine, phonebook, etc.)

How could I run an Archivevoice session?

Archivevoice borrows the same five-phase approach outlined in Vanese Apaza and Phoebe DeSantis’s 2016 Facilitator’s Toolkit for a Photovoice Project, with the authors’ permission. The five phases adapted to Archivevoice are as follows:

Phase 1: Introduction to Archivevoice

Introducing the method, the project and its research questions. Introduce participants to the archive or collection that will be explored, and the possible project outputs.


Phase 2: Selection of archival photos or other documentation



In Archivevoice, this phase replaces the photo-taking step in Photovoice. This is when the participants will receive archive research training tailored to the particular archive they are working with. They search the archive and select the photographs or other materials they wish to discuss. The project manager or lead researcher may want to preselect the items available for study (e.g. limiting the search within specific collections or setting specific inclusion criteria such as using materials with ‘no known copyright restrictions’).


Phase 3: Discussion around selected media

Just as in Apaza & DeSantis’ process, the “SHOWeD” method can be used to prompt the research participants to discuss the selected media.

SHOWeD is an acronym used in community-based health care research inspired by the pedagogical teachings of Paulo Freire. 

S – What things did you see?

H – What was happening?

O – Does this happen in our community?
W – Why does this happen?

D – What can we do about it?



While SHOWeD has a long history of being used in conjunction with Photovoice, other reflection and discussion methods such as focus groups, semi-structured interviews, and narrative writing are also possible or can be used in conjunction with SHOWeD depending on what is deemed appropriate or relevant by the lead researcher.

Phase 4: Media processing for archive activation

Here the selected archival media must be prepared for display. This could involve ensuring that the researcher has the appropriate rights or permissions to reproduce the identified media, that copyright is granted (or no known copyright restrictions are applied), and digitizing, formatting, and printing.

Phase 5: Community exhibition or other public output

Photovoice projects often culminate in a public exhibition for the community who were the subjects of the photos taken during the project. Similarly, once proper permissions are secured for the selected archival media, an exhibition of these items can be produced either physically – in the archive itself or gallery or community centre or online. Other options for public presentations of these selected media could be a book or zine, online exhibition, documentary, podcast, and more. 

Archivevoice serves as an adaptation of Photovoice to facilitate engagement with archival intervention and activation. According to Freire, “education must begin with the solution of the teacher-student contradiction, by reconciling the poles of the contradiction so that both are simultaneously teachers and students.” In this sense, a critical pedagogy requires both parties, “teachers” and “students” to understand that they each have something to learn from the other and that knowledge can be freely transferred from one to another. Participants who become de facto co-researchers in these projects in which they are given relative autonomy to express themselves through the selected media (e.g. photo, video, comic, archive, etc.) are empowered to have their feedback and knowledge heard and understood and they make planning and curatorial decisions. By elevating their status to co-researchers and collaborators, 

Archivevoice and the other -voice projects dissolve knowledge hierarchies asserting that lived experience and community knowledge merit their place in research and public pedagogy projects.  

For my final blog post—coming soon—I will report on my own investigation of the Archivevoice method, through a workshop I ran recently in Montreal with researchers from the Access in the Making Lab located at Concordia University in Montreal, Canada. Members from the lab are currently engaged in a project researching the disabling conditions that climate change and systems of extraction are having on various populations and ecosystems around the world. Together, we went through the steps of the Archivevoice method using Flickr as its source archive, looking for and discussing images that related to the researchers’ individual projects. The vast quantity of photos available in the Flickr archive prompted many interesting topics of discussion that will be explored in part three of this series.

Bibliography

Apaza, Vanesa, Phoebe Desantis, Aurea DeLeon, Jaclyn Keelin, Alexandra Ovits, Sherrine Schuldt, and Michael Spillane. “Facilitator’s Toolkit for a Photovoice Project.” United for Prevention in Passaic County and the William Paterson University Department of Public Health, 2016. https://www.up-in-pc.org/clientuploads/Whatwedo/Flyers/UPinPC_Photovoice_Facilitator_Toolkit_Final.pdf.

BAIRD, John Loige. “Comicvoice: Community Education through Sequential Art.” In POP CULTURE ASSOCIATION ANNUAL MEETING, Vol. 13, 2010.

Catalani, Caricia E. C. V., Anthony Veneziale, Larry Campbell, Shawna Herbst, Brittany Butler, Benjamin Springgate, and Meredith Minkler. “Videovoice: Community Assessment in Post-Katrina New Orleans.” Health Promotion Practice 13, no. 1 (January 1, 2012): 18–28. https://doi.org/10.1177/1524839910369070.

Freire, Paulo, Donaldo P. Macedo, Ira Shor, and Myra Bergman Ramos. Pedagogy of the Oppressed. 50th anniversary edition. 1 online resource (viii, 220 pages) vols. New York: Bloomsbury Academic, 2018. https://nls.ldls.org.uk/welcome.html?ark:/81055/vdc_100055048362.0x000001.

Shaffer, Roy. “Beyond the Dispensary.” English Press: Nairobi, Kenya, 1986. https://www.amoshealth.org/wp-content/uploads/sites/62/2019/10/Beyond-the-Dispensary.pdf.

Young, Kristen. “Black Community Archives in Practice.” In Black Community Archives in Practice, 211–21. McGill-Queen’s University Press, 2023. https://doi.org/10.1515/9780228019152-011.

 

Deep Reading in the Last Days of Summer

 

I joined the Foundation team in early August, with the long-term goal of better understanding future users of the Data Lifeboat project and Safe Harbor network. Thanks to the Digital Humanities Advancement Grant we were awarded by the National Endowment for the Humanities, my first task was to get up to speed with the Data Lifeboat project, a concept that has been in the works since 2022, as part of Flickr.org’s Content Mobility Program, and recently developed a working prototype. I have the structured independence to design my own research plan and, as every researcher knows, being able to immerse oneself in the topic prior, is a huge advantage. It allows us to frame the problem at hand, to be resolute with objectives and ground the research in what is known and current.

 

Stakeholder interviews

To understand what would be needed from the research plan, I first wanted to understand how we got to where we are with Data Lifeboat project.

I spoke with Flickr.org’s tight-knit internal team to gather perspectives that emphasised varying approaches to the question of long-term digital preservation: ranging from the technological, to the speculative, to the communal. It was curious to see how different team members viewed the project, each speaking from their own specialty, with their wider ambitions and community in mind.

Branching out, I enlisted external stakeholders for half-hour chats, those who’ve had a hand in the Data Lifeboat project since it was in napkin-scribble format. The tool owes its present form to a cadre of digital preservation experts and enthusiasts, who do not work on the project full-time, but have generously given their hours to partake in workshops, coffees, Whereby calls, and a blissfully meandering Slack thread. Knowing these folks would be, themselves, a huge repository of knowledge, I wanted a way to capture this. Besides introductions to the Safe Harbor Network co-design workshops (as supported by the recent Mellon Foundation grant) and my new role, I centred our conversation around three key questions:

1. What has your experience of the last six months of the Data Lifeboat project been like? How do you think we are doing? Any favourite moments, any concerns?
2. What are the existing practices around digital acquisition, storage and maintenance in your organisation(s)? How would the Data Lifeboat and Safe Harbor Network differ from the existing practices?
3. Where are the blind-spots that still exist for developing the Data Lifeboat project and Safe Harbor Network? What might we want to find out from the co-design workshops in October and November?

Here it was notable to learn what had stuck with them in the repose since the last Data Lifeboat project meet-up. For some the emphasis was on how the Data Lifeboat tool could connect institutions, for others it was how the technology can decentralise power and ownership of data. All were keen to see what shape the project would take next.

One point, however, remained amorphous to all stakeholders that we ought to carry forward into research: what is the problem that Data Lifeboat project is solving? Specifically in a non-emergency scenario (as the emergency need is intuitive). How can we best articulate that problem to our imagined users?

As our prototype user group is likely to be institutional users of Flickr (Galleries, Libraries, Archives and Museums), it will be important to meet them where they are, which brought me onto my next August task: the mini-literature review.

 

Mini Literature Review

Next, I wanted to get up to date on the contemporary discourses around digital preservation. Whilst stakeholders have brought their understanding of these topics to shaping the Data Lifeboat project, it felt as if the project was missing its own bibliography or set of citations. I wanted to ask, what are the existing conversations that Data Lifeboat project is speaking to?

It goes without saying that this is a huge topic and, despite my humble background in digital heritage research (almost always theoretical), cramming this all into one month would be impossible. Thus, I adopted the ethos of the archival ‘sliver’ that so informs the ethos of the Data Lifeboat project, to take a snapshot of current literature. After reviewing the writing to date on the project (shout-out to Jenn’s reporting here and here), I landed on three guiding topics for the literature review:

 

The Status of Digital Preservation

• What are the predominant tools and technologies of digital preservation?
• What are recent reflections and learnings from web archiving experiments?
• What are current institutional and corporate strategies to digital social collecting and long-term data storage?

Examples include:

• Preserving Digital Materials by Ross Harvey and Jaye Weatherburn
• The Archived Web: Doing History in the Digital Age by Niels Brügger
• Ten years of the UK web archive: what have we saved? by Andy Jackson

Care & Ethics of Archives

• What are the key ethical considerations among archivists today?
• How are care practices being embedded into archives and archival practice?
• What reflections and responses exist to previous ethical interventions?

Examples include:

• The Archive as a Productive Space of Conflict by Cedric Price
• Documenting the Now: Ethical Considerations for Archiving Social Media Content Generated by Contemporary Social Movements by Bergis Jules et al.
• An Introduction to Radical Empathy in Archival Practice by Elvia Arroyo-Ramírez, Jasmine Jones, Shannon O’Neill, and Holly Smith

Collaboration and Organisation in Archival Practice

• What are the infrastructures (hard and soft) of archival practice?
• What are the predominant organisational structures, considerations and difficulties in digital archives
• How does collaboration appear in archives? Who are the (visible and invisible) stakeholders?

Examples include:

• “Everything on the internet can be saved”: Archive Team, Tumblr and the cultural significance of web archiving’ by Jessica Ogden
• The invention of the archived web: tracing the influence of library frameworks on web archiving infrastructure by Kieran Hegarty
• Maintenance and Care by Shannon Mattern

 

A selection of academic articles, blog posts and industry guidelines were selected as source materials (as well as crowdsourcing from the Flickr.org team’s favourites). In reading these texts, I had top of mind the questions: ‘What does this mean for the Data Lifeboat project and the Safe Harbor Network’, in more granular terms this means, ‘What can we learn from these investigations?’ ‘Where are we positioned in the wider ecosystem of digital preservation?’ and finally, ‘What should we be thinking about that we aren’t yet?’

Naturally with more time, or with an academic audience in mind, a more rigorous methodology to discourse capture would be appropriate. For our purposes, however, this snapshot approach suffices – ultimately the data this research is grounded in comes not from textual problematising, but instead will emerge from our workshops with future users.

Having this resource is of huge benefit to meeting our session participants where they stand. Whilst there will inevitably be discourses, approaches and critiques I have missed, I will at least be able to speak the same language as our participants and get into the weeds of our problems in a complex, rather than baseline, manner. Furthermore, my ambition is for this bibliography to become an ongoing and open-source asset, expanding as the project develops.

These three headers (1. The Status of Digital Preservation, 2. Care & Ethics of Archives, 3. Collaboration and Organisation in Archival Practice) currently constitute placeholders for our workshop topics. It is likely, however, that these titles could evolve, splinter or coalesce as we come closer to a more refined and targeted series of questions for investigating with our participants.

 

Question Repository [in the works]

Concurrently to these ongoing workstreams, I am building a repository, or long-list, of questions for our upcoming workshops. The aim is to first go broad, listing all possible questions, in an attempt to capture as many inquisitive voices as possible. These will then be refined down, grouped under thematic headings which will in turn structure the sub-points or provocations for our sessions. This iterative process reflects a ground-up methodology, derived from interviews, reading, and the collective knowledge of the Flickr.org community, to finally land on working session titles for our October and November Safe Harbor Network co-design workshops.

Looking ahead, there is an opportunity to test several of these provocations around Data Lifeboat at our Birds-of-a-Feather session, taking place at this year’s International Conference on Digital Preservation (iPres) in Ghent later this month. Here we might foresee which questions generate lively and engaged discussion; which features of the Data Lifeboat tool and project prompt anticipation or concern; and finally, which pathways we ought to explore further.

 

Other things I’ve been thinking about this month

Carl Öhman’s concept of the Neo-Natufians in The Afterlife of Data: What Happens to Your Information When you Die and Why You Should Care

Öhman proposes that the digital age has ushered in a major shift in how we interact with our deceased. Referencing the Natufians, the first non-nomadic peoples to keep the dead among their tribe (who would adorn skulls with seashells and place them in the walls) instead of leaving them behind to the elements, he posits our current position is equally as seismic. The dead now live alongside us in the digital realm. A profound shift from the family shoebox of photographs, the dead are accessible from virtually anywhere at any time, their (visible and invisible) data trail co-existing with ours. An inescapable provocation for the Data Lifeboat project to consider.

“The imago mask, printed not in wax but in ones and zeros”

The Shikinen Sengu Ritual at Ise Jingu, Japan

The Shikinen Sengu is a ritual held at the Ise Grand Shrine in Japan every 20 years, where the shrine is completely rebuilt and the sacred objects are transferred to the new structure. This practice has been ongoing for over a millennium and makes me think on the mobility of cultural heritage (analogue or digital) and that stasis, despite its intuitive appeal, can cause objects to perish. I am reminded of the oft-exalted quote from di Lampedusa’s Sicilian epic:

“If we want things to stay as they are, things will have to change.” – The Leopard, by Giuseppe Tomasi di Lampedusa

Furthermore Shikinen Sengu highlights the importance of ritual in sustaining objects, despite the wear-and-tear that handling over millennia may cause. What might our rituals around digital cultural data be, what practices could we generate (even if the original impetus gets lost)?

 

Background Ephemera

Currently Playing: Laura Misch Sample the Earth and Sample the Sky

Currently Reading: The Hearing Trumpet by Leonora Carrington

Currently Drinking: Clipper Green Tea

This week we added maps to our Commons Explorer, and it’s proving to be a fun new way to find photos.

There are over 50,000 photos in the Flickr Commons collection which have location information telling us where the photo was taken. We can plot those locations on a map of the world, so you can get a sense of the geographical spread:

This map is interactive, so you can zoom in and move around to focus on a specific place. As you do, we’ll show you a selection of photos from the area you’ve selected.

You can also filter the map, so you see photos from just a single Commons member. For smaller members the map points can tell a story in themselves, and give you a sense of where a collection is and what it’s about:

These maps are available now, and know about the location of every geotagged photo in Flickr Commons.

Give them a try!

How can you add a location to a Flickr Commons photo?

For the first version of this map, we use the geotag added by the photo’s owner.

If you’re a Flickr Commons member, you can add locations to your photos and they’ll automatically show up on this map. The Flickr Help Center has instructions for how to do that.

It’s possible for other Flickr members to add machine tags to photos, and there are already thousands of crowdsourced tags that have location-related information. We don’t show those on the map right now, but we’re thinking about how we might do that in future!

How does the map work?

There are three technologies that make these maps possible.

The first is SQLite, the database engine we use to power the Commons Explorer. We have a table which contains every photo in the Flickr Commons, and it includes any latitude and longitude information. SQLite is wicked fast and our collection is small potatoes, so it can get the data to draw these maps very quickly.

I’d love to tell you about some deeply nerdy piece of work to hyper-optimize our queries, but it wasn’t necessary. I wrote the naïve query, added a couple of column indexes, and that first attempt was plenty fast. Tallying the locations for the entire Flickr Commons collection takes ~45ms; tallying the locations for an individual member is often under a millisecond.)

The second is Leaflet.js, a JavaScript library for interactive maps. This is a popular and feature-rich library that made it easy for us to add a map to the site. Combined with a marker clustering plugin, we had a lot of options for configuring the map to behave exactly as we wanted, and to connect it to Flickr Commons data.

The third is OpenStreetMap. This is a world map maintained by a community of volunteers, and we use their map tiles as the backdrop for our map.

Plus ça Change

To help us track changes to the Commons Explorer, we’ve added another page: the changelog.

This is part of our broader goal of archiving the organization. Even in the six months since we launched the Explorer, it’s easy to forget what happened when, and new features quickly feel normal. The changelog is a place for us to remember what’s changed and what the site used to look like, as we continue to make changes and improvements.

I had the wonderful opportunity to visit London and Flickr Foundation HQ during the month of May 2024. The first month of my fellowship was a busy one, getting settled in, meeting the team, and making contacts around the UK to share and develop my idea for a new qualitative research method that was inspired by my perusing of just a minuscule fraction of the billions of photos uploaded and visible on Flickr.com.

Unlike the brilliant and techno-inspired minds of my Flickr Foundation cohort: George, Alex, Ewa, and Eryk, my head is often drifting in the clouds (the ones in the actual sky) or deep in books, articles, and archives. Since rediscovering Flickr and contemplating its many potential uses, I have activated my past work as a researcher, artist, and cultural worker, to reflect upon the ways Flickr could be used to engage communities in various visual and digital ethnographies.

Stemming from anthropology and the social sciences more broadly, ethnography is a branch of qualitative research involving the study of cultures, communities, or organizations. A visual ethnography thereby employs visual methods, such as photography, film, drawing, or painting.. Similarly, digital ethnography refers to the ethnographic study of cultures and communities as they interact with digital and internet technologies.

In this first post, I will trace a nonlinear timeline of different community-based and academic research projects I have conducted in recent years. Important threads from each of these projects came together to form the basis of the new ethnographic method I have developed over the course of this fellowship, which I call Archivevoice. 

Visual representations of community

The research I conducted for my masters thesis was an example of a digital, visual ethnography. For a year, I observed Instagram accounts sharing curated South Asian visual media, analyzing the types of content they shared, the different media used, the platform affordances that were engaged with, the comments and discussions the posts incited, and how the posts reflected contemporary news, culture, and politics. I also interviewed five people whose content I had studied. Through this research I observed a strong presence of uniquely diasporic concerns and aesthetics. Many posts critiqued the idea of different nationhoods and national affiliations with the countries founded after the partition of India in 1947 – a violent division of the country resulting in mass displacement and human casualty whose effects are still felt today. Because of this violent displacement and with multiple generations of people descended from the Indian subcontinent living outside of their ancestral territory, among many within the community, I observed a rejection of nationalist identities specific to say India, Pakistan, or Bangladesh. Instead, people were using the term “South Asian” as a general catchall for communities living in the region as well as in the diaspora. Drawing from queer cultural theorist José Esteban Muñoz, I labelled this digital, cultural phenomenon I observed “digital disidentification.”[1] 

My explorations of community-based visual media predate this research. In 2022, I worked with the Montreal grassroots artist collective and studio, Cyber Love Hotel, to develop a digital archive and exhibition space for 3D-scanned artworks and cultural objects called Things+Time. In 2023, we hosted a several-week-long residency program with 10 local, racialized, and queer artists. The residents were trained on archival description and tagging principles, and then selected what to archive. The objects curated and scanned in the context of this residency were in response to the overarching theme loss during the Covid-19 pandemic, in which rampant closures of queer spaces, restaurants, nightlife, music venues, and other community gathering spaces were proliferating across the city.

During complete pandemic lockdown, while working as the manager for cultural mediation at the contemporary gallery Centre CLARK, I conducted a similar project which involved having participants take photographs which responded to a specific prompt. In partnership with the community organization Head & Hands, I mailed disposable cameras to participants from a Black youth group whose activities were based at Head & Hands. Together with artist and CLARK member, Eve Tangy, we created educational videos on the principles of photography and disposable camera use and tasked the participants to go around their neighbourhoods taking photos of moments that, in their eyes, sparked Black Joy—the theme of the project. Following a feedback session with Eve and myself, the two preferred photos from each participants’ photo reels were printed and mounted as part of a community exhibition entitled Nous sommes ici (“We’re Here”) at the entry of Centre CLARK’s gallery. 


These public community projects were not formal or academic, but, I came to understand each of these projects as examples of what is called research-creation (or practice-based research or arts-based research). Through creative methods like curating objects for digital archiving and photography, I, as the facilitator/researcher, was interested in how the media comprising each exhibition would inform myself and the greater public about the experiences of marginalized artists and Black youth at such pivotal moments in these communities.

Photovoice: Empowering research participants

The fact that both these projects involved working with a community and giving them creative control over how they wanted their research presented reminded me of the popular qualitative research method used often within the fields of public health, sociology, and anthropology called Photovoice. The method was originally coined as Photo Novella in 1992 and then later renamed Photovoice in 1996 by researchers Caroline Wang and Mary Ann Burris. The flagship study that established this method for decades involved scholars providing cameras and photography training to low-income women living in rural villages of Yunnan, China.

The goals of this Photovoice research were to better understand, through the perspectives of these women, the challenges they faced within their communities and societies, and to communicate these concerns to policymakers who might be more amenable to photographic representations rather than text. Citing Paulo Freire, Wang and Burris note the potential photographs have to raise consciousness and promote collective action due to their political nature. [5]

According to Wang and Burris, “these images and tales have the potential to reach generations of children to come.” [6] The images created a medium through which these women were able to share their experiences and also relate to each other. Even with 50 villages represented in the research, shared experience and strong reactions to certain photographs came up for participants – including this picture of a young child lying in a field while her mother farmed nearby. 

According to the authors, “the image was virtually universal to their own experience. When families must race to finish seasonal cultivating, when their work load is heavy, and when no elders in the family can look after young ones, mothers are forced to bring their babies to the field. Dust and rain weaken the health of their infants… The photograph was a lightening [sic] rod for the women’s discussion of their burdens and needs.” [8]

Since its conception in the 1990s as a means for participatory needs assessment, many scholars and researchers have expanded Photovoice methodology. Given the exponential increase of camera access via smartphones, Photovoice is an increasingly feasible method for this kind of research. Recurring themes in Photovoice work include community health, mental health studies, ethnic and race-based studies, research with queer communities, as well as specific neighbourhood and urban studies. During the pandemic lockdowns, there were also Photovoice studies conducted entirely online, thus giving rise to the method of virtual Photovoice. [9]

Critical Fabulation: Filling the gaps in visual history

Following my masters thesis research, I became more interested in how communities sought to represent themselves through photography and digital media. Not only that, but also how communities would form and engage with content circulated on social media – despite these people not being the originators of this content. 

In my research, people reacted most strongly to family photographs depicting migration from South Asia to the Global North. Although reasons for emigration varied across the respondents, many people faced similar challenges with the immigration process and resettlement in a new territory. They shared their experiences through commenting online. 

People in communities which are underrepresented in traditional archives are often forced to work with limited documentation. They must do the critical and imaginative work of extrapolating what they find. While photographs can convey biographical, political, or historical meaning, exploring archived images with imagination can foster creative interpretation to fill gaps in the archival record. Scholar of African-American studies, Saidiya Hartman, introduced the term “critical fabulation” to denote this practice of reimagining the sequences of events and actors behind the narratives contained within the archive. In her words, this reconfiguration of story elements, attempts “to jeopardize the status of the event, to displace the received or authorized account, and to imagine what might have happened or might have been said or might have been done.” [10] In reference to depictions of narratives from the Atlantic slave trade in which enslaved people are often referred to as commodities, Hartman writes “the intent of this practice is not to give voice to the slave, but rather to imagine what cannot be verified, a realm of experience which is situated between two zones of death—social and corporeal death—and to reckon with the precarious lives which are visible only in the moment of their disappearance. It is an impossible writing which attempts to say that which resists being said (since dead girls are unable to speak). It is a history of an unrecoverable past; it is a narrative of what might have been or could have been; it is a history written with and against the archive.” [11]

I am investigating what it means to imagine the unverifiable and reckoning what only becomes visible at its disappearance. In 2020, I wrote about Facebook pages serving as archives of queer life in my home town, Montreal. [12] For this study, I once again conducted a digital ethnography, this time of the event pages surrounding a QTPOC (queer/trans person of colour)-led event series known as Gender B(l)ender. Drawing from Sam McBean, I argued that simply having access to these event pages on Facebook creates a space of possibility in which one can imagine themselves as part of these events, as part of these communities – even when physical, in-person participation is not possible. Although critical fabulation was not a method used in this study, it seemed like a precursor to this concept of collectively rethinking, reformulating, and resurrecting untold, unknown, or forgetting histories of the archives. This finally leads us to the project of my fellowship here at the Flickr Foundation.

In addition to this fellowship, I am coordinator of the Access in the Making Lab, a university research lab working broadly on issues of critical disability studies, accessibility, anti-colonialism, and environmental humanities. In my work, I am increasingly preoccupied with the question of methods: 1) how do we do archival research—especially ethical archival research—with historically marginalized communities; and, 2) how can research “subjects” be empowered to become seen as co-producers of research. 

I trace this convoluted genealogy of my own fragmented research and community projects to explain the method I am developing and have proposed to university researchers as a part of my fellowship. Following my work on Facebook and Instagram, I similarly position Flickr as a participatory archive, made by millions of people in millions of communities. [13] Eryk Salvaggio, fellow 2024 Flickr Foundation research fellow, also positions Flickr as an archive such that it “holds digital copies of historical artifacts for individual reflection and context.” [14] From this theoretical groundwork of seeing these online social image/media repositories as archives, I seek to position archival items – i.e. the photos uploaded to Flickr.com – as a medium for creative interpretation by which researchers could better understand the lived realities of different communities, just like the Photovoice researchers. I am calling this set of work and use cases “Archivevoice”.

In part two of this series, I will explore the methodology itself in more detail including a guide for researchers interested in engaging with this method.

Footnotes

[1] Prakash Krishnan, “Digital Disidentifications: A Case Study of South Asian Instagram Community Archives,” in The Politics and Poetics of Indian Digital Diasporas: From Desi to Brown (Routledge, 2024), https://www.routledge.com/The-Politics-and-Poetics-of-Indian-Digital-Diasporas-From-Desi-to-Brown/Jiwani-Tremblay-Bhatia/p/book/9781032593531.

[2] Caroline Wang and Mary Ann Burris, “Empowerment through Photo Novella: Portraits of Participation,” Health Education Quarterly 21, no. 2 (1994): 171–86.

[3] Kunyi Wu, Visual Voices, 100 Photographs of Village China by the Women of Yunnan Province, 1995.

[4] Wu.

[5] Caroline Wang and Mary Ann Burris, “Photovoice: Concept, Methodology, and Use for Participatory Needs Assessment,” Health Education & Behavior 24, no. 3 (1997): 384.

[6] Wang and Burris, “Empowerment through Photo Novella,” 179.

[7] Wang and Burris, “Empowerment through Photo Novella.”

[8] Wang and Burris, 180.

[9] John L. Oliffe et al., “The Case for and Against Doing Virtual Photovoice,” International Journal of Qualitative Methods 22 (March 1, 2023): 16094069231190564, https://doi.org/10.1177/16094069231190564.

[10] Saidiya Hartman, “Venus in Two Acts,” Small Axe 12, no. 2 (2008): 11.

[11] Hartman, 12.

[12] Prakash Krishnan and Stefanie Duguay, “From ‘Interested’ to Showing Up: Investigating Digital Media’s Role in Montréal-Based LGBTQ Social Organizing,” Canadian Journal of Communication 45, no. 4 (December 8, 2020): 525–44, https://doi.org/10.22230/cjc.2020v44n4a3694.

[13] Isto Huvila, “Participatory Archive: Towards Decentralised Curation, Radical User Orientation, and Broader Contextualisation of Records Management,” Archival Science 8, no. 1 (March 1, 2008): 15–36, https://doi.org/10.1007/s10502-008-9071-0.

[14] Eryk Salvaggio, “The Ghost Stays in the Picture, Part 1: Archives, Datasets, and Infrastructures,” Flickr Foundation (blog), May 29, 2024, https://www.flickr.org/the-ghost-stays-in-the-picture-part-1-archives-datasets-and-infrastructures/.

Bibliography

Hartman, Saidiya. “Venus in Two Acts.” Small Axe 12, no. 2 (2008): 1–14.

Huvila, Isto. “Participatory Archive: Towards Decentralised Curation, Radical User Orientation, and Broader Contextualisation of Records Management.” Archival Science 8, no. 1 (March 1, 2008): 15–36. https://doi.org/10.1007/s10502-008-9071-0.

Krishnan, Prakash. “Digital Disidentifications: A Case Study of South Asian Instagram Community Archives.” In The Politics and Poetics of Indian Digital Diasporas: From Desi to Brown. Routledge, 2024. https://www.routledge.com/The-Politics-and-Poetics-of-Indian-Digital-Diasporas-From-Desi-to-Brown/Jiwani-Tremblay-Bhatia/p/book/9781032593531.

Krishnan, Prakash, and Stefanie Duguay. “From ‘Interested’ to Showing Up: Investigating Digital Media’s Role in Montréal-Based LGBTQ Social Organizing.” Canadian Journal of Communication 45, no. 4 (December 8, 2020): 525–44. https://doi.org/10.22230/cjc.2020v44n4a3694.

Oliffe, John L., Nina Gao, Mary T. Kelly, Calvin C. Fernandez, Hooman Salavati, Matthew Sha, Zac E. Seidler, and Simon M. Rice. “The Case for and Against Doing Virtual Photovoice.” International Journal of Qualitative Methods 22 (March 1, 2023): 16094069231190564. https://doi.org/10.1177/16094069231190564.

Salvaggio, Eryk. “The Ghost Stays in the Picture, Part 1: Archives, Datasets, and Infrastructures.” Flickr Foundation (blog), May 29, 2024. https://www.flickr.org/the-ghost-stays-in-the-picture-part-1-archives-datasets-and-infrastructures/.

Wang, Caroline, and Mary Ann Burris. “Empowerment through Photo Novella: Portraits of Participation.” Health Education Quarterly 21, no. 2 (1994): 171–86.

———. “Photovoice: Concept, Methodology, and Use for Participatory Needs Assessment.” Health Education & Behavior 24, no. 3 (1997): 369–87.

Wu, Kunyi. Visual Voices, 100 Photographs of Village China by the Women of Yunnan Province, 1995.

“Definitions belong to the definers, not the defined.”
― Toni Morrison, Beloved

Generative Artificial Intelligence is sometimes described as a remix engine. It is one of the more easily graspable metaphors for understanding these images, but it’s also wrong. 

As a digital collage artist working before the rise of artificial intelligence, I was always remixing images. I would do a manual search of the public domain works available through the Internet Archive or Flickr Commons. I would download images into folders named for specific characteristics of various images. An orange would be added to the folder for fruits, but also round, and the color orange; cats could be found in both cats and animals. 

I was organizing images solely on visual appearance. It was anticipating their retrieval whenever certain needs might emerge. If I needed something round to balance a particular composition, I could find it in the round folder, surrounded by other round things: fruits and stones and images of the sun, the globes of planets and human eyes. 

Once in the folder, the images were shapes, and I could draw from them regardless of what they depicted. It didn’t matter where they came from. They were redefined according to their anticipated use. 

A Churning

This was remixing, but I look back on this practice with fresh eyes when I consider the metaphor as it is applied to diffusion models. My transformation of source material was not merely based on their shapes, but their meaning. New juxtapositions emerged, recontextualizing those images. They retained their original form, but engaged in new dialogues through virtual assemblages. 

As I explore AI images and the datasets that help produce them, I find myself moving away from the concept of the remix. The remix is a form of picking up a melody and evolving it, and it relies on human expression. It is a relationship, a gesture made in response to another gesture.

To believe we could “automate” remixing assumes too much of the systems that do this work. Remixes require an engagement with the source material. Generative AI systems do not have any relationship with the meanings embedded into the materials they reconfigure. In the absence of engagement, what machines do is better described as a churn, combining two senses of the word. Generative AI models churn images in that they dissolve the surface of these images. Then it churns out new images, that is, “to produce mechanically and in great volume.” 

Of course, people can diffuse the surface meaning of images too. As a collagist, I could ignore the context of any image I liked. We can look at the stereogram below and see nothing but the moon. We don’t have to think about the tools used to make that image, or how it was circulated, or who profited from its production. But as a collagist, I could choose to engage with questions that were hidden by the surfaces of things. I could refrain from engagements with images, and their ghosts, that I did not want to disturb. 

Actions taken by a person can model actions taken by a machine. But the ability to automate a person’s actions does not suggest the right or the wisdom to automate those actions. I wonder if, in the case of diffusion models, we shouldn’t more closely scrutinize the act of prising meaning from an image and casting it aside. This is something humans do when they are granted, or demand, the power to do so. The automation of that power may be legal. But it also calls for thoughtful restraint. 

In this essay, I want to explore the power to inscribe into images. Traditionally, the power to extract images from a place has been granted to those with the means to do so. Over the years, the distribution and circulation of images has been balanced against those who hold little power to resist it. In the automation of image extraction for training generative artificial intelligence, I believe we are embedding this practice into a form of data colonialism. I suggest that power differentials haunt the images that are produced by AI, because it has molded the contents of datasets, and infrastructures, that result in those images. 

The Crying Child

Temi Odumosu has written about the “digital reproduction of enslaved and colonized subjects held in cultural heritage collections.” In The Crying Child, Odumosu looks at the role of the digital image as a means of extending the life of a photographic memory. But this process is fraught, and Odumosu dedicates the paper to “revisiting those breaches (in trust) and colonial hauntings that follow photographed Afro-diasporic subjects from moment of capture, through archive, into code” (S290). It does so by focusing on a single image, taken in St. Croix in 1910: 

“This photograph suspends in time a Black body, a series of compositional choices, actions, and a sound. It represents a child standing alone in a nondescript setting, barefoot with overpronation, in a dusty linen top too short to be a dress, and crying. Clearly in visible distress, with a running nose and copious tears rolling down its face, the child’s crinkled forehead gives a sense of concentrated energy exerted by all the emotion … Emotions that object to the circumstances of iconographic production.”

The image emerges from the Royal Danish Library. It was taken by Axel Ovesen, a military officer who operated a commercial photography business. The photograph was circulated as a postcard, and appears in a number of personal and commercial photo albums Odumosu found in the archive.

The unnamed crying child appeared to the Danish colonizers of the island as an amusement, and is labeled only as “the grumpy one” (in the sense of “uncooperative”). The contexts in which this image appeared and circulated were all oriented toward soothing and distancing the colonizers from the colonized. By reframing it as a humorous novelty, the power to apply and remove meaning is exercised on behalf of those who purchase the postcard and mail it to others for a laugh. What is literally depicted in these postcards is, Odumosi writes, “the means of production, rights of access, and dissemination” (S295). 

I am describing this essay at length because the practice of categorizing this image in an archive is so neatly aligned with the collection and categorization of training data for algorithmic images. Too often, the images used for training are treated solely as data, and training defended as an act that leaves no traces. This is true. The digital copy remains intact.

But the image is degraded, literally, step by step until nothing remains but digital noise. The image is churned, the surface broken apart, and its traces stored as math tucked away in some vector space. It all seems very tidy, technical, and precise, if you treat the image as data. But to say so requires us to agree that the structures and patterns of the crying child in the archive — the shape of the child’s body, the details of the wrinkled skin around the child’s mouth — are somehow distinct from the meaning of the image. 

Because by diffusing these images into an AI model, and pairing existing text labels to it within the model, we extend the reach of Danish colonial power over the image. For centuries, archives have organized collections into assemblages shaped and informed by a vision of those with power over those whose power is held back. The colonizing eye sets the crying child into the category of amusements, where it lingers until unearthed and questioned.

If these images are diffused into new images — untraceable images, images that claim to be without context or lineage — how do we uncover the way that this power is wielded and infused into the datasets, the models, and the images ultimately produced by the assemblage? What obligations linger beneath the surfaces of things? 

Every Archive a Collage

Collage can be one path for people to access these images and evaluate their historical context. The human collage maker, the remixer, can assess and determine the appropriateness of the image for whatever use they have in mind. This can be an exercise of power, too, and it ought to be handled consciously. It has featured as a tool of Situationist detournement, a means of taking images from advertising and propaganda to reveal their contradictions and agendas. These are direct confrontations, artistic gestures that undermine the organization of the world that images impose on our sense of things. The collage can be used to exert power or challenge the status quo. 

Every archive is a collage, a way of asserting that there is a place for things within an emergent or imposed structure. The scholar and artist Beth Coleman’s work points to the reversal of this relationship, citing W.E.B. Du Bois’ exhibition at the 1900 Paris Exposition. M. Murphy writes,

“Du Bois’s use of [photographic] evidence disrupted racial kinds rather than ordered them … Du Bois’s exhibition was crucially not an exhibit of ‘facts’ and ‘data’ that made black people in Georgia knowable to study, but rather a portrait in variation and difference so antagonistic to racist sociology as to dislodge race as a coherent object of study” (71).

The imposed structures of algorithmically generated images rely on facts and data, defined a certain way. They struggle with context and difference. The images these tools produce are constrained to the central tendencies of the data they were trained on, an inherently conformist technology. 

To challenge these central tendencies means to engage with the structures it imposes on this data, and to critique this churn of images into data to begin with. Matthew Fuller and Eyal Weizman describe “hyper-aesthetic” images as not merely “part of a symbolic regime of representation, but actual traces and residues of material relations and of mediatic structures assembled to elicit them” (80). 

Consider the stereoscope. Once the most popular means of accessing photographs, the stereoscope relied on a trick of the eye, akin to the use of 3D glasses. It combined two visions of the same scene taken from the slight left and slight right of the other. When viewed through a special viewing device, the human eye superimposes them, and the overlap creates the illusion of physical depth in a flat plane. We can find some examples of these on Flickr (including the Danish Film Museum) or at The Library of Congress’ Stereograph collection.

The time period in which this technology was popular happened to overlap with an era of brutal colonization, and the archival artifacts of this era contain traces of how images projected power. 

I was struck by stereoscopic images of American imperialism in the Philippines during the US occupation, starting in 1899. They aimed to “bring to life” images of Filipino men dying in fields and other images of war, using the spectacle of the stereoscopic image as a mechanism for propaganda. These were circulated as novelties to Americans on the mainland, a way of asserting a gaze of dominance over those they occupied.

In the long American tradition of infotainment, the stereogram fused a novel technological spectacle with the effort to assert military might, paired with captions describing the US cause as just and noble while severely diminishing the numbers of civilian casualties. In Body Parts of Empire : Visual Abjection, Filipino Images, and the American Archive, Nerissa Balce writes that

“The popularity of war photographs, stereoscope viewers, and illustrated journals can be read as the public’s support for American expansion. It can also be read as the fascination for what were then new imperial ‘technologies of vision’” (52).

The link between stereograms as a style of image and the gaze of colonizing power is now deeply entrenched into the vector spaces of image synthesis systems. Prompt Midjourney for the style of a stereogram, and this history haunts the images it returns. Many prompted images for “Stereograms, 1900” do not even render the expected, highly formulaic structure of a stereogram (two of the same images, side by side, at a slight angle). It does, however, conjure images of those occupied lands. We see a visual echo of the colonizing gaze.  

Images produced for the more generally used “stereoview,” even without the use of a date, still gravitate to a similar visual language. With “stereoview,” we are given the technical specifics of the medium. The content is more abstract: people are missing, but strongly suggested. These perhaps get me closest to the idea of a “haunted” image: a scene which suggests a history that I cannot directly access.

Perhaps there are two kinds of absences embedded in these systems. The people that colonizers want to erase, and then the evidence of the colonizers themselves. Crucially, this gaze haunts these images. 

Here are four sets of two pairs.

These styles are embedded into the prompt for the technology of image capture, the stereogram. The source material is inscribed with the gaze that controlled this apparatus. The method of that inscription — the stereogram — inscribes this material into the present images.  The history is loaded into the keyword and its neighboring associations in the vector space. History becomes part of the churn. These are new old images, built from the associations of a single word (stereoview) into its messy surroundings.

It’s important to remember that the images above are not documents of historical places or events. They’re “hallucinations,” that is, they are a sample of images from a spectrum of possible images that exists at the intersection of every image labeled “stereoview.” But “stereoview” as a category does not isolate the technology from how it was used. The technology of the stereogram, or the stereoviewer, was deeply integrated into regimes of war, racial hierarchies, and power. The gaze, and the subject, are both aggregated, diffused, and made to emerge through the churning of the model.

Technologies of Flattening

The stereoview and the diffusion models are both technologies of spectacle, and the affordance of power to those who control it is a similar one. They are technologies for flattening, containing, and re-contextualizing the world into a specific order. As viewers, the generated image is never merely the surfaces of photography churned into new, abstract forms that resemble our prompts. They are an activation of the model’s symbolic regime, which is derived from the corpus of images because it has the power to isolate images from their meaning. 

AI has the power of finance, which enables computational resources that make obtaining 5 billion images for a dataset possible, regardless of its impact on local environments. It has the resources to train these images; the resources to recruit underpaid labor to annotate and sort these images. The critiques of AI infrastructure are numerous.

I am most interested here in one form of power that is the most invisible, which is the power of naturalizing and imposing an order of meaning through diffused imagery. The machine controls the way language becomes images. At the same time, it renders historical documentation meaningless — we can generate all kinds of historical footage now.

These images are reminders of the ways data colonialism has become embedded within not merely image generation but the infrastructures of machine learning. The scholar Tiara Roxanne has been investigating the haunting of AI systems long before me. In 2022 Roxanne noted that,

“in data colonialism, forms of technological hauntings are are experienced when Indigenous peoples are marked as ‘other,’ and remain unseen and unacknowledged. In this way, Indigenous peoples, as circumscribed through the fundamental settler-colonial structures built within machine learning systems, are haunted and confronted by this external technological force. Here, technology performs as a colonial ghost, one that continues to harm and violate Indigenous perspectives, voices, and overall identities” (49).

AI can ignore “the traces and residues of material relations” (Fuller and Weizman) as it reduces the image to its surfaces instead of the constellations of power that structured the original material. These images are the product of imbalances of power in the archive, and whatever interests those archives protected are now protected by an impenetrable, uncontestable, automated set of decisions steered by the past.

The Abstracted Colonial Subject

What we see in the above images are an inscription by association. The generated image, as a type of machine learning system, matters not only because of how it structures history into the present. It matters because it is a visualization that reaches to something far greater about automated decision making and the power it exerts over others. 

These striations of power in the archive or museum, in the census or the polling data, in the medical records or the migration records, determine what we see and what we do not. What we see in generated images must contort itself around what has been excluded from the archives. What is visible is shaped by the invisible. In the real world, this can manifest as families living on a street serving as an indication of those who could not live on that street. It could be that loans granted by an algorithmic assessment always contain an echo of loans that were not approved. 

The synthetic image visualizes these traces. They churn the surfaces, not the tangled reality beneath them. The images that emerge are glossy, professional, saturated. Hiding behind these products by and for the attention economy is the world of the not-seen. What are our obligations as viewers to the surfaces we churn when we prompt an image model? How do we reconcile our knowledge of context and history with the algorithmic detachment of these automated remixes?

The media scholar Roland Meyer writes that,

“[s]omewhere in the training data that feeds these models are photographs of real people, real places, and real events that have somehow, if only statistically, found their way into the image we are looking at. Historical reality is fundamentally absent from these images, but it haunts them nonetheless.”

In a seance, you raise spirits you have no right to speak to. The folly of it is the subject of countless warnings in stories, songs and folklore. 

What if we took the prompt so seriously? What if typing words to trigger an image was treated as a means of summoning a hidden and unsettled history? Because that is what the prompt does. It agitates the archives. Sometimes, by accident, it surfaces something many would not care to see. Boldly — knowing that I am acting from a place of privilege, and power, I ask the system to return “the abstracted colonial subject of photography.” I know I am conjuring something I should not be. 

My words are transmitted into the model within a data center, where they flow through a set of vectors, the in-between state of thousands of photographs. My words are broken apart into key words — “abstracted, colonial, colonial subject, subject, photography.” These are further sliced into numerical tokens to represent the mathematical coordinates of these ideas within the model. From there, these coordinates offer points of cohesion which are applied to find an image within a jpg of digital static. The machine removes the noise toward an image that exists in the overlapping space of these vectors.

Avery Gordon, whose book Ghostly Matters is a rich source of thinking for this research, writes:

“… if there is one thing to be learned from the investigation of ghostly matters, it is that you cannot encounter this kind of disappearance as a grand historical fact, as a mass of data adding up to an event, marking itself in straight empty time, settling the ground for a future cleansed of its spirit” (63).

If history is present in the archives, the images churned from the archive disrupt our access to the flow of history. It prevents us from relating to the image with empathy, because there is no single human behind the image or within it. It’s the abstracted colonial gaze of power applied as a styling tool. It’s a mass of data claiming to be history.

Human and Mechanical Readings

I hope you will indulge me as my eye wanders through the resulting image.

I am struck by the glossiness of it. Midjourney is fine-tuned toward an aesthetic dataset, leaning into images found visually appealing based on human feedback. I note the presence of palm trees, which brings me to the Caribbean Islands of St. Croix where The Crying Child photograph was taken. I see the presence of barbed wire, a signifier of a colonial presence.

The image is a double exposure. It reminds me of spirit photography, in which so-called psychic photographers would surreptitiously photograph a ghostly puppet before photographing a client. The image of the “ghost” was superimposed on the film to emerge in the resulting photo. These are associations that come to my mind as I glance at this image. I also wonder about what I don’t know how to read: the style of the dress, the patterns it contains, the haircut, the particulars of vegetation.

We can also look at the image as a machine does. Midjourney’s describe feature will tell us what words might create an image we show it. If I use it with the images it produces, it offers a kind of mirror-world insight into the relationship between the words I’ve used to summon that image and the categories of images from which it was drawn.

To be clear, both “readings” offer a loose, intuitive methodology, keeping in the spirit of the seance — a Ouija board of pixel values and text descriptors. They are a way in to the subject matter, offering paths for more rigorous documentation: multiple images for the same prompt, evaluated together to identify patterns and the prevalence of those patterns. That reveals something about the vector space. 

Here, I just want to see something, to compare the image as I see it to what the machine “sees.”

The image returned for the abstract colonial subject of photography is described by Midjourney this way: 

“There is a man standing in a field of tall grass, inverted colors, tropical style, female image in shadow, portrait of bald, azure and red tones, palms, double exposure effect, afrofuturist, camouflage made of love, in style of kar wai wong, red and teal color scheme, symmetrical realistic, yellow infrared, blurred and dreamy illustration.”

My words produced an image, and then those words disappeared from the image that was produced. “Colonized Subject” is adjacent to the words the machine does see: “tall grass,” “afrofuturism,” “tropical.” Other descriptions recur as I prompt the model over and over again to describe this image, such as “Indian.” I have to imagine that this idea of colonized subjects “haunts” these keywords. The idea of the colonial subject is recognized by the system, but shuffled off to nearest synonyms and euphemisms. Might this be a technical infrastructure through which the images are haunted? Could certain patterns of images be linked through unacknowledged, invisible categories the machine can only indirectly acknowledge? 

I can only speculate. That’s the trouble with hauntings. It’s the limit to drawing any conclusions from these observations. But I would draw the reader’s attention to an important distinction between my actions as a collage artist and the images made by Midjourney. The image will be interpreted by many of us, who will find different ways to see it, and a human artist may put those meanings into adjacency through conscious decisions. But to create this image, we rely solely on a tool for automated churning.

We often describe the power of images in terms of what impact an image can have on the world. Less often we discuss the power that impacts the image: the power to structure and give the image form, to pose or arrange photographic subjects. 

Every person interprets an image in different ways. A machine makes images for every person from a fixed set of coordinates, its variety constrained by the borders of its data. That concentrates power over images into the unknown coordination of a black box system. How might we intervene and challenge that power?  

The Indifferent Archivist 

We have no business of conjuring ghosts if we don’t know how to speak to them. As a collage artist, “remixing” in 2016 meant creating new arrangements from old materials, suggesting new interpretations of archival images. I was able to step aside — as a white man in California, I would never use the images of colonized people for something as benign as “expressing myself.” I would know that I could not speak to that history. Best to leave that power to shift meanings and shape new narratives to those who could speak to it. Nonetheless, it is a power that can be wielded by those who have no rights to it.  

Yes, by moving any accessible image from the online archive and transmuting it into training data, diffusion models assert this same power. But it is incapable of historic acknowledgement or obligation. The narratives of the source materials are blocked from view, in service to a technically embedded narrative that images are merely their surfaces and that surfaces are malleable. At its heart is the idea that the context of these images can be stripped and reduced into a molding clay, for anyone’s hands to shape to their own liking. 

What matters is the power to determine the relationships our images have with the systems that include or exclude. It’s about the power to choose what becomes documented, and on what terms. Through directed attention, we may be able to work through the meanings of these gaps and traces. It is a useful antidote to the inattention of automated generalizations. To greet the ghosts in these archives presents an opportunity to intervene on behalf of complexity, nuance, and care.

That is literal meaning of curation, at its Latin root: “curare,” to care. In this light, there is no such thing as automated curation.

Reclaiming Traceability

In 2021, Magda Tyzlik-Carver wrote “the practice of curating data is also an epistemological practice that needs interventions to consider futures, but also account for the past. This can be done by asking where data comes from. The task in curating data is to reclaim their traceability and to account for their lineage.”

When I started the “Ghost Stays in the Picture” research project, I intended to make linkages between the images produced by these systems and the categories within their training data. It would be a means of surfacing the power embedded into the source of this algorithmic churning within the vector space. I had hoped to highlight and respond to these algorithmic imaginaries by revealing the technical apparatus beneath the surface of generated images. 

In 2024, no mainstream image generation tool offers the access necessary for us to gather any insights into its curatorial patterns. The image dataset I initially worked with for this project is gone. Images of power and domination were the reason — specifically, the Stanford Internet Observatory’s discovery of more than 3,000 images in the LAION 5B dataset depicting abused children. Realizing this, the churn of images became visceral, in the pit of my stomach. The traces of those images, the pain of any person in the dataset, lingers in the models. Perhaps imperceptibly, they shape the structures and patterns of the images I see.

In gathering these images, there was no right to refuse, no intervention of care. Ghosts, Odumosu writes, “make their presences felt, precisely in those moments when the organizing structure has ruptured a caretaking contract; when the crime has not been sufficiently named or borne witness to; when someone is not paying attention” (S299). 

The training of Generative Artificial Intelligence systems has relied upon the power to automate indifference. And if synthetic images are structured in this way, it is merely a visualization of how “artificial intelligence systems” structure the material world when carelessly deployed in other contexts. The synthetic image offers us a glimpse of what that world would look like, if only we would look critically at the structures that inform its spectacle. If we can read algorithmic decision-making a lapse in care, a disintegration of accountability, we might see fresh pavement has been poured onto sacred land. 

This regime of Artificial Intelligence is not an inevitability. It is not even a single ideology. It is a computer system, and computer systems, and norms of interaction and participation with those systems, are malleable. Even with training datasets locked away behind corporate walls, it might still be possible “to insist on care where there has historically been none” (Odumosu S297), and by extension, to identify and refuse the automated inscription of the colonizing ghost.

 

This post concludes my research work at the Flickr Foundation, but I am eager to continue it. I am seeking publishers of art books, or curators for art or photographic exhibitions, who may be interested in a longer set of essays or a curatorial project that explores this methodology for reading AI generated images. If you’re interested, please reach out to me directly: eryk.salvaggio@gmail.com.

“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.  

Representativeness

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. 

Calibration

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.  

Attribution

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 “Flickr.com 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.