AI and Ways of Seeing: Q&A with Lauren Tilton

This year, the Center for Digital Humanities celebrates its tenth anniversary with the theme “Humanities for AI.” Through this series of events, projects, and conversations, we explore how humanistic values and approaches are crucial in developing, using, and interpreting the field of “AI.”

As part of this initiative, we welcomed Lauren Tilton (Director, Distant Viewing Lab; E. Claiborne Robins Professor of Liberal Arts and Digital Humanities, Department of Rhetoric and Communications, University of Richmond) to Princeton to present her lecture “Distant Viewing: AI and Ways of Seeing” on October 21. In this talk, she introduced the concept of distant viewing and demonstrated how AI is animating humanistic inquiry through examples from visual culture. To expand upon her reflections on “how digital humanities, data science, and the larger field of AI could reshape the world together,” we invited Tilton to respond to a set of questions related to AI and its impact on humanities scholarship and her work in visual culture and data science.

What does “Humanities for AI” mean for you?

I find the idea to be a nice broad framing that centers on the role of the humanities in shaping AI. AI is often celebrated through a capitalistic, economic, and technological lens that centers on profit, innovation, and progress. Less frequently do we ask: Why are we building X and what is the social impact? Who is profiting, and who isn’t? Innovation according to whom? Progress from whose perspective? The humanities, particularly areas such as American Studies and Science and Technology Studies (STS), offer important frameworks, theories, and histories to address these questions. The humanities also allow us to imagine more creative possibilities for our AI future.

You have been developing the concept of “distant viewing” for some time. How is this project changing in light of developments in generative AI (genAI)?

Distant Viewing is a theory of how the computational exploration of digital images through the application of computer vision works, and why it is needed.

We think Distant Viewing (DV) is key to developments in genAI. Computer vision is critical to multimodal large language models (MMLLMs). DV offers a theory for understanding computer vision, a way to identify and interrogate the ways of seeing that we are building in these algorithms engaged in the act of distant viewing. Unpacking this component is vital to better understanding how MMLLMs are working. They are trained in specific ways of seeing (such as training sets that feature photography from the last two decades and classification of people). They encode specific visual cultures and then generate from there.

We see this now with MMLLMs. When Midjourney launched, they produced a specific vision of women: young, busty, and thin with flowing hair. They looked like a combination of anime (in style) and Western beauty standards (in physical features). As one thread Reddit r/midjourney discussed, the contributors were struggling to generate an "[u]gly, plain-faced, ordinary, awkward features, hideous, unattractive, gross…” (actual prompt) woman, for users found the tool kept generating “pinup-grade attractive” women. A particular idea of beauty was baked into the MMLLM, reiterating problematic ideas of “women” and female beauty through long-critiqued tropes.

At the same time, I think distant viewing offers us a theory for how we can continue to build different kinds of algorithms. The theory offers a way to critically (not necessarily negatively, but critique as careful analysis) read algorithms and imagine other ways of viewing what we want to design. DV, therefore, opens creative possibilities for us.

You work at the intersections of visual culture and data science. What fruitful and exciting avenues of research is AI introducing for our understanding of visual semiotics, and media studies more broadly?

The Distant Viewing Lab primarily studies photography, television, and film. In some cases, we are focused on a direct question that animates media studies such as narrative arc, visual style, and changes in form and messages over time. AI strategies have included using algorithms out of the box, such as image segmentation and image embedding, and we’ve also designed our algorithms, such as a shot boundary detector bundled inside the Distant Viewing Toolkit. For example, we are working on a large corpus of TV sitcoms to explore the “genre” over several decades. We started this work several years ago with our article on Bewitched and I Dream of Jeannie in Cultural Analytics with scholar and LA Review of Books film editor Annie Berke (make sure to follow her brilliant commentary on all things TV and film!). The work became the basis of a chapter in Distant Viewing (MIT Press, 2023, Open Access). We are now looking at elements such as pacing, characters on-screen, shot type, and other features to look at developments in the genre over time. Recent developments in AI and processing power make the ability to analyze at this scale possible.

All the talk right now is about LLMs. MMLLMs offer exciting new avenues for us to engage in media studies. We use them for our sitcom work, and another significant part of our work focuses on the public humanities. We are deeply committed to supporting access and discovery of cultural heritage collections to help open the rich histories yet to be told. We have also been helping them assess AI needs, where AI might support institutional goals. One project is ADDI: Access and Discovery of Documentary Images, where we assessed the power of specific AI approaches to open access across five collections with approximately 300,000 photographs. Working with the nation’s cultural heritage institutions, such as the Library of Congress and the Smithsonian, is a real privilege. I believe in our nation’s institutions, particularly the power of cultural heritage institutions, to tell new, silenced, and unexpected stories about who “we” are and where “we” have been. Being a part of supporting these institutions’ commitments to open access and serving such a wide range of publics has been a highlight of my career.

You direct the Distant Viewing Lab, as well as the Center for Liberal Arts and AI (launching fall 2025), both at the University of Richmond. How has running a “lab” influenced how you see collaborative scholarship in the humanities? And how will this new research center expand on that?

The experience of building DH projects, co-authoring articles and books, as well as running a lab and soon a center has instilled in me the power of collaborative scholarship. I find collaboration to be enriching. We’ve generated more nuanced, interdisciplinary, and cutting-edge scholarship, and I’ve had the opportunity to learn from brilliant colleagues. Collaboration is now at the center of CLAAI. Mainly, when one works at smaller institutions or institutions with limited resources, collaboration is one way to be stronger than the sum of our parts. CLAAI is built on this idea. By bringing together the strengths of each of the 15 undergraduate-focused small liberal arts colleges that are a part of the Associated Colleges of the South, I think we will be better positioned to expand and amplify cutting-edge teaching and research in AI. Undoubtedly, one needs to develop skills such as patience, openness, and trust to collaborate, but it has been worth it!

You are currently the co-president of the Association for Computers and the Humanities (ACH). What are some ways that ACH can play a role in this AI moment? 

We are currently working in several areas and welcome the community’s advice! One area is access to data. ACH has supported the University of California at Berkeley’s Samuelson Law, Technology & Public Policy Clinic and Authors Alliance's incredible work to help researchers access digital materials such as ebooks and DVDs subject to digital rights management, or DRM. (For more, Quinn Dombrowski and I wrote an article about the work of ACH in Digital Studies/ Le champ numérique). We have a new AI + DH Working Group led by Quinn Dombrowski and CDH’s Meredith Martin! The group will meet during ACH2025 to discuss the next steps, and all conference attendees are welcome to join. Another area that we are turning to is the environmental impact of AI, and we hope to launch a working group on this topic soon.

What is your greatest concern and biggest hope for the future of AI for humanities scholarship?

So many concerns and hopes!

One concern is the environmental impact of genAI, and our scholarship being a part of the problem rather than solution. There is a lot of attention on this issue right now, and we need to continue to make this issue central. I’m hopeful we will find a more sustainable way forward. At a minimum, there seem to be significant economic and corporate incentives for companies like Microsoft to find a better energy solution (assuming they want to stay in business for decades and have a market). This may be where renewable energy flourishes, even if significantly driven by significant power users and corporate logic.

One of my biggest hopes is that we continue to use AI in ways that ignite and support students’ and colleagues’ interests in the humanities. At their best, the humanities offer theories, histories, and ways of being that make us aware of how people have experienced and are experiencing the world. There is also a reality that students are often very interested in AI, for it is a technology that is shaping their every day. I see AI as one way we can animate our curiosity about the past and present. Digital humanities offer an exciting intersection well-positioned to continue at the forefront of these possibilities.

Lauren Tilton is the E. Claiborne Robins Professor of Liberal Arts and Professor of Digital Humanities in the Department of Rhetoric and Communications at the University of Richmond. She also directs the Distant Viewing Lab. Her research focuses on analyzing, developing, and applying digital and computational methods to the study of 20th and 21st-century documentary expression and visual culture. Her primary scholarship incorporates theoretical and methodological approaches from American Studies, Media Studies, Public Humanities, and Data Science. She is committed to interdisciplinary, collaborative, open-access scholarship. She earned a PhD in American Studies from Yale University. She is currently the co-president of the Association for Computers and the Humanities (ACH), the scholarly association for digital humanities in the United States.