Author: Miranda Hynes

Week Three, 9/2/2026

In class this week, we’re considering how artificial intelligence could aid or impact digital collections. To preface, I am generally skeptical of the utility of AI (particularly consumer-facing AI like Chat GPT), and am one of many who are concerned about the kind of future the technology might bring about. Even if you set aside the massive ecological impact that AI will have on our planet—which is a lot to set aside—the daily use of these technologies holds clear ethical issues. It is frequently implemented to devalue human labor, devalue learning, and to infringe upon artistic ownership. Nonetheless, the technology itself is one of many new digital tools, and in the right hands it can do really interesting things. When considering viable and meaningful implementations of AI, one that immediately comes to mind for me is archival work.

In one sense, digital archives (and data management in general) present an ideal use case for machine learning/AI. The job of a digital archivist is to sort large amounts of data and metadata, often with different media types linked to one object. One thing that machine learning is good at, even the consumer-facing large language models, is compiling huge amounts of data and making sense of it in a manner that a person can more easily digest. The other major task of an archivist is to identify what an object, artwork, or document actually is; of course, image recognition is another popular use of AI technologies.

However, the focus for this week was not on how AI might be able to aid the internal work of archivists, but rather, how it can aid us in creating more dynamic, accessible, user interfaces. In particular, two of the digital projects we read about each utilized AI to showcase the formal similarities between different images in a larger cluster of images. One project was Imgs.AI, which allows users to search pre-existing collections—like the Met or the Rijksmuseum—by choosing images that they’d like to see more similar objects to.¹ The other project, “Training the Archive,” utilized a similar technique, wherein machine learning models were trained to make visual associations between images before generating a map or a schematic, grouping similar images with one another. Both projects take a human’s ability to recognize similar images, and an art historian’s finely honed ability to do so, and automates it to the end of quickly generating a schematic of images. 

Though the project’s stated ambition was to create an intuitive, fun, and automated website to sort images, Imgs.AI was relatively disappointing: its interface was confusing, not clearly labeled, and the sorting capabilities rarely seemed more advanced than your typical digital collection. 

In the other AI digital collections project, “Training the Archive,” the machine learning model was trained from theories of the German art historian Aby Warburg (1866-1929). In order to train this model, project leader Dominik Bönisch notes that: “a procedure had to be implemented that would enable curators to connect digital collection objects with one another according to specific (…) criteria for context, aesthetics, iconography, and art historical references.”² (Bönisch, 26)

Though many of the graphics generated from this project accurately picked up on formal and aesthetic nuances, the machine learning process still relied heavily on the expertise, and labor, of human professionals.

At the present moment, it seems that there is a distinct ceiling on what machine learning can contribute to digital archives, digitization, and user interfaces, and further, I believe that there will continue to be. As Dr. Bauer noted during class, each and every one of these projects still require human intervention, and I expect this will continue to be the case. At the very least, humans still need to be present as editors and fact-checkers. Though Bönisch ends his article by declaring that “AI is also suitable for the automated processing of the collections.” (Bönisch, 29), I am not so sure that this is currently the case. Further, digital collections managers and archivists have been creating dynamic and impactful user-interfaces without the use of AI for decades now.

Each of the impressive digital archiving projects referenced by Tim Sherratt in his 2011 article  “It’s all about the Stuff: Collections, Interfaces, Power and People,” were created prior to the popularization of artificial intelligence, and the expertise of curators and archivists come to the fore in each of these projects. 

Virginia Untold: The African American Narrative is one particularly effective example of how archivists can make records legible and accessible to audiences. By providing high-quality scans, grouping items together by theme or relevant historical event, and attaching explanatory texts to collections, archivists shaped legal documents and letters into an accessible narrative for general audiences and researchers alike. Users are also able to easily save certain documents to read for later by “pinning” them to their account.

As alluded to in my last posts, digitization is a laborious and often boring process. That being said, it is a process that is completed by interns, students, and volunteers, alongside professional archivists with library science degrees. I fear that what is at stake here is not the implementation of AI to make those laborers’ jobs easier and more impactful to the public, but rather, a proposition to replace them with machine labor as much as possible. Though a novel and potentially useful technology, the introduction of AI and machine learning will not solve the systemic issues that our field continues to face. Prior to introducing these new technologies, we ought to re-assess the technologies we already use, and give archivists the resources and training needed in order to be digitally literate. In my opinion, the systemic underfunding of our field is at the heart of this struggle to create exciting and accessible user interfaces.

1. Fabian Offert and Peter Bell, “Imgs.AI. A Multimodal Search Engine For Digital Art History,” International Journal for Digital Art History (vol. 9, 2024) ︎
2. Dominik Bönisch. “The Curator’s Machine: Clustering of Museum Collection Data Through Annotation of Hidden Connection Patterns Between Artworks.” Digital Art History Journal (May 4, 2021) ︎

From left to right: Brigid Hynes-Cherin, Peter Hynes, Arleen Hynes, and Timothy John Hynes

Week two: 8/25/2026

“The nature of the gap between original and surrogate may change as technology improves, but it can never be entirely closed. In fact, the strengths of digital surrogates lie in their differences: their versatility, their independence from time and place, their potential for creative
interaction.” — Emma Stanford in “A Field Guide to Digital Surrogates”

Any museum professional, archivist, or librarian will be able to recognize the significance of digitization, or “digital surrogates,” to the field today. Immense resources, labor, and time have already been dedicated to the digitization of all manner of documents and it’s unlikely that demand for such projects will stop soon. From an accessibility and preservation standpoint, the benefit to a digitized archive is readily apparent. Information held on physical documents can now be preserved where it otherwise might have been at risk of damage or degradation. People from across the globe can now view documents and artifacts held at archives, libraries, and museums, so long as they have access to the internet. There are so many more benefits to well-done digitization projects, like the potential to easily transcribe and translate digitized documents with OCR.

Of course, it is worth interrogating any technology or methodology used by archivists and art historians, particularly when so many resources are directed their way (at times, at the risk of other kinds of projects). In “A Field Guide to Digital Surrogates: Evaluating and Contextualizing a Rapidly Changing Resource,” scholar Emma Stanford outlines the long history of “surrogates” (or, images of art objects) as an essential resource to art historians, while also making a close evaluation of all the complications and pitfalls that this populate this practice in the digital age. Institutional structures, and failings, account for many of these—for example, it is impossible for most institutions to digitize all of their archives, leaving the decision of which objects and documents should be digitized as a curatorial one, and therefore impacted by many of the same factors that continuously shape curatorial decisions. In the case of the Bodleian Libraries at the University of Oxford, this meant that more manuscripts were digitized according to what interested big donors. Even on a smaller scale, editing through omission is one huge way that digitization can distort documents. Stanford points out a number of examples: long manuscripts which only have a few “important” pages digitized, how digitized images often crop the objects and documents they capture, or the inability of photography to capture the tactility of objects and papers.

In reality, so many of the issues that plague digitization efforts have to do with funding and labor, even though Stanford does not always acknowledge this outright. What archives and museums can actually afford to have a lengthy and costly digitization project depends largely on which institutions have funding, which is largely dependent on complex socio-economic factors. There is also the question of usability and access once the archives are actually digitized. As lengthy a process that scanning and archiving digitized documents can be, designing the interfaces through which users access these documents is just as extensive. The ability (or inability) to search for accurately described objects is at the center of what makes a digitized archived accessible (or not). Ideally, each archive would be able to provide extensive tags for objects, and would reliably be able search from the metadata of objects, but this is often not the case. When I designed a digital archive in Omeka for a collection at my undergraduate university, I was certainly not leaving detailed tags for each item. I was the only research assistant working on the project and I simply did not have time, nor was I being paid enough, to make the archive as accessible and searchable as I would have liked it to be.

Of the above issues with digitization efforts, most are somewhat solvable given enough time and money. There’s one issue that remains genuinely irreconcilable: that a digital surrogate will never be able to capture the physical object in its entirety. Art historians know this well, which is why they often advise students to see works of art in person if at all possible. Stanford believes that rather than being a weakness, the inherent difference of a digital surrogate can be a strength:

“In fact, the strengths of digital surrogates lie in their differences: their versatility, their independence from time and place, their potential for creative interaction. The most effective digital surrogates leverage these traits.” (Stanford, 211).

Recently, I have been able to see these strengths with a newfound emotional potency. My cousin and I are currently embarking on a digitization project for my late Aunt Brigid’s family and travel photos, of which there are thousands. Such a project is a serious archival task, but it’s allowed me to view the work of digitization through new eyes, outside the confines of my professional life. I have a large family (21 first cousins), and I can feel how much it means to everyone to have these images of loved ones, so many of whom have passed, accessible and preserved for future generations.

While nothing can replace the affective experience of holding one of these photographs in your hands, this project can open up new opportunities for family members to see their history where otherwise we might have to wait for another family reunion, wedding, or funeral to view them. When I approach this topic from a critical art historical perspective, I see many of the gaps and failures of digital surrogates as a sign to be wary of them. However, this project has given me assurance of the real-life significance of digitization. Whether or not it’s apparent in the moment, you never know the importance digital surrogates might hold to a researcher or a private individual.

Ultimately, the project of digitization is an important and worthwhile one, but it’s equally important to consider the labor of museum professionals, interns, and volunteers, many of whom may be victim to the exploitative labor practices of a field that so often lacks the money and resources to back otherwise worthy ambitions. It is with this institutional structure in mind that we could continue to acknowledge the limitations of digitization, while advocating for the laborers doing their best to surmount them.

week one: 8/18/2026

“The struggles and wishes of our age are, undeniably, wrapped up in computational systems.” —Amanda Waiselewski and Anna Naslund in “Critical Digital Art History”

Digital Art History (or DAH) can refer to both the tools that art historians might use, and a way of thinking about art history. For this week in our seminar on “Alt-Methods: Digital Art History,” we were assigned a number of readings that sought to understand the history behind DAH and what it means to be a practitioner of “digital art history” today. As a defined discipline, it is far newer than art history proper; of course, the very tools that would produce DAH only came about with the widespread popularization of computers. Yet every author we read this week is quick to note just how important technology, whether digital or not, has always been to art historical research. Alison Langmead writes in her “Art and Architectural History and the Performative, Mindful Practice of the Digital Humanities,” that the adoption of the slide projector in the late-19th and early-20th centuries as represented a major shift in the field. Pioneered by art history professor Herman Grimm in the 1890’s, the projector (initially called a “Magic Lantern”) presented opportunities for art historians to teach simultaneous to students and lecture attendants viewing works of art. For a field that relies on the often unsteady ability of words to accurately represent images, it’s understandable that such a device was adopted quickly. Langmean continues, noting that throughout the 20th century, the slide room and the projector continued to be the main tools of art historians and art history teachers. Significantly, not only were these technologies incredibly useful to art historical research, the neccessity of a physical slide library where art historians (often professors and students at universities), were allowed a kind of third-space to meet and discuss their work.

What followed the overwhelming switch to digital cameras, screens, and computers was not a shift away from the use of images and projectors in art historical teaching, but rather, the abandonment of slides and slide rooms. DAH practitioners Harald Klinke and Paul Jaskot both note this discipline-wide shift to the digital as one of many ways in which all art historians are already “digital art historians,” despite the field as a whole tending to look down on novel and creative uses of digital technologies in research. As an art history graduate student, I can attest to the fact that despite my professors’ consistent reliance on digital images, computers, and projectors, very few of them seem to be entirely comfortable with digital technologies and most research outputs are the same as decades past (like research papers or manuscripts). Klinke proposes that art historians already dealing with digital data on a regular basis ought to look to DAH as a means of improving their data literacy: what he defines as the ability to understand and critique where, how, and why data was generated, and further, the ability to recognize the role of algorithms in the filtering of information.

Of course, most of DAH is conducted through projects that use data in digital systems which generate a myriad of formats. My experience in archival roles has given me a greater familiarity with DAH projects than my time as an art history student has. Because archives are interested in making a large amount of data accessible and interesting to the public, digital formats can present so many opportunities. I’ve worked on a couple different projects that had to do with digitizing archives and creating interfaces for audiences to view and sort the contents of the archives. One consideration in these contexts is how we (as archivists) can make our collections engaging, and formats like Omeka are really well suited to that. Another major consideration is that, often, only one researcher at a time can view archival documents in person unless they’ve been included in a physical exhibition. For both art historians and archivists, the tools proposed by DAH can present so many opportunities to expand our audiences. Much like slide rooms in the 1890’s, images and data can be democratized and made accessible through digital technologies. Paul Jaskot even argues that the opportunity to present information as maps might make art historical data itself more “democratized,” in the sense that where a paper typically requires a single narrative, the visualization data offers more potential loose associations.

Nonetheless, these visual data sets do still offer opportunities to present those single narratives—for better or worse. Maps, graphs, 3-D visualizations, and digital exhibits are all subject to distortion and narrativization by their authors. This is similarly true of the digital image which, like the slide image, is only one possible representation of an object. The central issue of reproductions versus reality presented in Walter Benjamin’s seminal “The Work of Art in the Age of Mechanic Reproduction” (1935) remains truer than ever in our digital age. Yet, I would hope that art historians can embrace the digital technologies that we are already using, consider using new ones, and even begin to build data literacy in themselves and their students. The world is certainly not going to become less dominated by computers and digital data anytime soon.