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In 2022, generative artificial intelligence made its popular breakthrough. Most will have heard about ChatGPT, a conversational model capable of responding to natural language input in a remarkably human-like fashion, but the range of applications is much wider. A couple of developers recently figured out how to use text-to-image generation to create music directly from a text prompt. In Copenhagen, a theatre staged a play where generative AI played a leading role on stage.
The play was a new version of an old H. C. Andersen tale about a young scholar whose shadow acquires a mind of its own and enslaves its master. The scholar was played by a human, the shadow by the AI. Each night before the show, the model tuned itself through live conversation with the audience.
I was in the audience on the last night. The director came onstage beforehand and introduced it as a “techno-anthropological experiment.” Then we began chatting with the AI and it felt as if we began to build rapport with it. It was hard to not subsequently watch the improvisation unfolding on stage in light of that experience. As an audience, we were left wondering how to make sense of this new companion species walking among us.
Ethnography now faces a situation like the one it faced 20 years ago with the emergence of virtual online worlds. A new field has suddenly come into being with its own cultural expressions, its own species of interlocutors, and its own peculiar conditions for doing fieldwork. The question is no longer what it means to grow up and acquire friendships and identities in Second Life or World of Warcraft (as for Tom Boellstorff and Bonnie Nardi), but how do you learn to see the world like an artificial intelligence that has been raised in a specific data world?
Deep neural networks are as unexplainable as any human informant and with the advent of generative AI they are being put to creative use in ways where having a particular view of the world is no longer so much of a bias as it is a feature. Ethnography is going to be necessary if we are to understand and live well with these beings.
Coming of age in Stable Diffusion
A marriage ceremony in Stable Diffusion takes place outdoors. The bride and groom stand facing each other on a lawn surrounded by trees. The ceremony is performed by a person holding a set of papers, typically a man in a suit, and occasionally under a floral arch or canopy. The bride wears a white gown, the groom a suit or black tie. If the marriage is same sex, both parties are identically dressed. Flowers adorn men’s buttonholes and are carried as bouquets by the women. The venue has open skies and lush vegetation. You never see fallen leaves or winter-clad landscapes, and there is rarely a building in sight. There might be a spectacular view—to the sea, for example—but it is not the rule. Being outside in natural surroundings seems to be the main thing.
It is not that indoor weddings are completely absent. If you ask specifically about a church wedding, Stable Diffusion knows what it looks like. It knows that the bride and groom stand by the altar during the ceremony while the guests sit down on the benches, and that the guests stand up while the bride and groom walk up and down the aisle. The same is true for Hindu, Jewish, or Muslim weddings: Stable Diffusion can describe them, if specifically prompted to do so, and knows that dress codes and settings change. But those types of ceremonies are not the default association when you ask what a wedding looks like; it is not what you will be shown when you ask for a photo of, say, a “wedding ceremony for my daughter” or “your brother getting married to your sister-in-law.”
A conversation about marriage in Stable Diffusion takes for granted that there is a particular way of doing things (the outdoor ceremony described above) and you therefore have the clear impression that you are having that conversation with someone (or something) that speaks from a particular position in the world. What is not clear is where that partial perspective comes from.
Beyond bias
An often-voiced assumption about the peculiar specificity of something like a Stable Diffusion marriage is that it reflects a cultural bias in the training data. Stable Diffusion is an AI model that generates images from a text prompt. As such, it belongs to a family of generative AIs like DALL-E 2, Midjourney, or Disco Diffusion that all had their popular breakthrough in the latter half of 2022. Trained on vast amounts of image-caption pairs scraped from the web, these models create new images that are either photorealistic, in the style of known painters, art movements, historical periods, or, if you engineer the prompt properly, some hybridized, never-before-seen genre.
While the dress code at a Stable Diffusion wedding suggests at least a Western bias in the training data, the outdoor setting is not as obvious in this regard. As it turns out, rather than simply reproducing some existing cultural pattern in the training data, Stable Diffusion also seems to be producing its own.
Stable Diffusion was trained on an English language subset of LAION-5B, an open data set of five billion image-text pairings published in March 2021 by the Large-Scale Artificial Intelligence Open Network, a German nonprofit which has been a leading provider of training data for the current breed of generative AI models. The data can be freely downloaded or searched directly online.
Interestingly, a browse through wedding-related queries reveals no apparent likeness to a marriage ceremony in Stable Diffusion. Much of it takes place indoors and although the white wedding gown is often prevalent, the dress code is much more varied in the training data than it is in the model output. In some instances, such as for the generic query “a marriage ceremony,” Hindu dress is dominant. One must be careful here since only images with English descriptions were used for training. By Stable Diffusion’s own admission this in itself “affects the overall output of the model, as white and Western cultures are often set as the default,” but even if you ignore images with non-English captions the pattern seems to be the same.
The composition of the images also differs considerably between training data and model. In a Stable Diffusion marriage, you typically see full-body shots of groups of up to 10 people; in LAION-5B you find anything from close-ups of a wedding invitation to birds-eye shots of a dance floor. In general, one cannot discern what a typical marriage ceremony is supposed to look like by simply browsing the training data, regardless of the acknowledged English language bias. Whatever marriage has become in Stable Diffusion, it has become so somewhere between LAION-5B, the deep neural network that helps Stable Diffusion turn natural language into visual features, and the diffuser model that Stable Diffusion uses to create new images corresponding to those features.
And beyond transparency
Thus, trying to understand marriage from the point of view of Stable Diffusion takes us into the territory of (un)explainable AI. It is no secret how the AI works. It is designed around a diffuser model that turns random visual noise into images with visual features resembling a text prompt inputted by the user. That, in turn, is made possible by a neural network called CLIP (Contrastive Language-Image Pre-training) which was first released by US-based OpenAI in January 2021.