Seeing, Describing, and Imagining: Human and Machine Vision in the Humanities

Framing the Workshop: Vision, Interpretation, and Context

In recent years, digital tools have quietly transformed how we experience and interpret images in museums, classrooms, and research settings. As an art historian working at the intersection of art history, digital media, and visual culture, I am drawn to examining how people translate visual experience into words, and how that process compares with machine analysis of the same images. I am especially interested in creating spaces that invite us to pause, pay closer attention, and make the act of interpretation visible, rather than treating images or technologies as self-evident.

Seeing, Describing, and Imagining originated from a simple, low-stakes classroom exercise I first encountered while serving as a teaching assistant in a course on formal and visual analysis taught by my advisor. Watching students work through the challenge of turning what they were seeing into words made it clear how tentative and negotiable description can be. That experience stayed with me and prompted me to rethink the exercise beyond the classroom, adapting it into a workshop format.

The workshop aims to create a shared, practice-based method for visual analysis that can be applied in various settings, from visual analysis courses to digital humanities labs, while staying rooted in art-historical approaches to looking.

From Looking to Language: Description and Interpretation

The workshop is conceived as a hands-on, collaborative way of exploring how images move between seeing, describing, and imagining. It is designed to begin with a simple exercise. Participants would look closely at an artwork and translate what they see into words. Working in pairs, one person would study the artwork and describe it in detail, while the other would create a quick line sketch using only that description, without ever seeing the image itself.

This phase aims to slow the process in a constructive way. Participants are encouraged to reflect on the act of describing itself: What do you choose to mention first, and why? Which parts of the artwork are hardest to put into words? These questions are designed to show that description is never neutral. Emphasis, order, and omission all influence how an image is understood.

When sketches and original artworks are placed side by side, the workshop is designed to shift from creating to comparing. Instead of viewing differences as mistakes, participants are encouraged to explore what moments of similarity and difference may reveal about the connection between image and text. The aim is not to fix these gaps but to use them as a way to think about how seeing, knowing, and describing are linked in art history practice.

Human–Machine Translation: AI, Images, and Visual Convention

Starting from this analog foundation, the workshop is structured to move into a digital phase by introducing AI text-to-image systems. Participants would revisit and refine their descriptions before entering them into an AI model such as DALL·E or Adobe Firefly. The resulting AI-generated image would then be placed alongside the original artwork and the participant-created sketch as a third object for comparison.

Rather than evaluating which image is better or more accurate, this stage emphasizes observation. Participants are encouraged to ask what kinds of visual patterns might emerge across AI-generated images. Which elements seem emphasized, simplified, or made more uniform across different outputs? Looking across multiple results is meant to create space for noticing patterns without assuming in advance what those patterns will be.

Existing scholarship by authors such as Kate Crawford, Safiya Umoja Noble, Ruha Benjamin, and Johanna Drucker suggests that AI systems are shaped by the datasets they are trained on, the ways information is classified, and the cultural assumptions embedded in those systems. Drawing on these works, the workshop is designed to create conditions where such influences could become visible through hands-on engagement rather than explanation. As participants compare images, the process opens up the possibility of exploring whether familiar visual conventions emerge, particularly when prompts involve artworks or visual traditions that are not widely represented in large image datasets. What becomes noticeable is deliberately left open and expected to take shape through comparison rather than as a predetermined outcome.

The workshop also introduces a reverse process, moving from image to text. Participants would upload an artwork into an AI vision tool and examine how the system translates the image into language. Reading these AI-generated descriptions alongside participants’ own interpretive accounts is intended to prompt reflection on differences in tone, emphasis, and confidence, and to raise questions about how uncertainty functions in human versus machine descriptions.

Staying with the Process: Open-Ended Inquiry and Reflection

Taken together, Seeing, Describing, and Imagining is framed as an open-ended inquiry rather than a demonstration. Prompt writing and refinement are approached not as purely technical tasks but as interpretive acts, similar to the analytical frameworks art historians use when working with images. While elements of the workshop align with existing practices in art history education, digital humanities, and critical AI studies, Seeing, Describing, and Imagining brings these approaches together in a distinctive sequence that foregrounds interpretation as an active, negotiated process involving both human and machine systems of vision.

The workshop is designed to foster attentiveness, curiosity, and careful comparison. It encourages participants to stay with the process and to observe what may emerge as images move between eyes, words, algorithms, and back again. In this way, both human and machine vision are presented not as stable endpoints, but as ongoing, context-dependent practices shaped by history, culture, and interpretation.

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Works Cited

• Benjamin, Ruha. Race After Technology: Abolitionist Tools for the New Jim Code. Cambridge, UK: Polity Press, 2019.
• Crawford, Kate. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven, CT: Yale University Press, 2021.
• Drucker, Johanna. Graphesis: Visual Forms of Knowledge Production. Cambridge, MA: Harvard University Press, 2014.
• Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press, 2018.