Navigating the Turbulent Waves of Artistic Innovation
Humans are experiencing an acute technological shock with the eruption of new image technologies powered by generative artificial intelligence (GenAI). While entrepreneurs may view this moment as disrupting business as usual, and investors may try to anticipate the unpredictable path of such disruption, this article sets aside entrepreneurial and economic perspectives to probe the current cultural turmoil around GenAI.
As seasoned IT professionals, well-versed in providing practical tips and in-depth insights on technology, we recognize that artists and art historians bring a unique perspective to this cultural technology. AI deals with human languages, images, and the priors of human perception, shaping (and in some cases forcing) our understandings of the world. The impact of GenAI on the visual arts is comparable to earlier historical moments of technological shock, when literary and visual artists grappled with unprecedented reproductive tools such as the printing press, photography, and cinema.
By examining how artists metabolized these once radical inventions in the past, we can gain insights into navigating the current wave of technoshock. We’ll explore how contemporary artists are using, adapting, or confronting GenAI tools in their creative practice, and how artists and designers might address known issues of bias in these systems. Ultimately, we argue that GenAI must be shaped to yield greater public good, curbing unsustainable and undesirable patterns before they become entrenched.
Technoshock and the Metabolization of Radical Media
Technological shock epochs are highly salient inflection points in histories of art, literature, and music. Over the centuries, artistic discourses have ranged from techno-utopianism to techno-pessimism, echoed today by the popular phrase coupling the “promise and peril” of GenAI. But such views are not exclusive to our present moment.
The genealogy of probability propositions fueling visual AI programs can be traced back millennia, to early divination manuals and philosophical experiments that alternately advanced and critiqued such “combinatorial arts” in a quest for higher truth, beauty, predictive power, or knowledge. Particularly salient were medieval texts exploring the Arabic algorithmic process of Zairja (“letter magic”), informing Ramon Llull’s fourteenth-century Ars Magna (“Ultimate General Art”) and Gottfried Leibniz’s seventeenth-century Dissertatio de arte combinatoria (“Dissertation on Combinatorial Art”).
British writer and satirist Jonathan Swift was aware of these precedents, and his 1726 work of fiction, Gulliver’s Travels, creates an encounter with a knowledge “engine” that combines sets of symbols, phrases, and numerals—conceptual precursors to today’s image training sets. Swiftʼs narrative addressed the rampant production of plagiarized and altered texts issuing from the monopoly that the British crown had granted to the medieval Stationersʼ Guild, which censored, copied, and generated texts without permission or quality control from human writers.
Today, GenAI raises similar questions in the field of visual arts (as well as literary and musical arts not addressed here). Responses to GenAI have varied by generation and privilege, ranging from the apocalyptic to the sublime—a typical response to technological shock more generally. Historically, technoshock only becomes culturally productive through phases of encounter, critical pushback, adaptation, and modification.
Early Adopters and the Shaping of New Media
Early adopters of new technologies often play a crucial role in the cultural metabolization of these tools, generating, consuming, or circulating new media in order to produce receptive publics. A well-documented instance is the development of photography in the mid-nineteenth century, which ushered in a period of technoshock spanning more than a century.
At photography’s inception, Frederick Douglass—American orator, abolitionist, and statesman—put into practice how the new technology could make a visual culture for all, arguing that “the humbled servant girl whose income is but a few shillings per week may now possess a more perfect likeness of herself than noble ladies and court royalty.” Access to photography for Douglass meant claiming and democratizing the artistic genre of portraiture, which could reveal both new forms of political celebrity and true images of Black Americans that countered the violence of racist stereotypes and cartoons.
Douglass’s use of photography is a powerful example of how new media adopters can shape the future of a cultural technology. Photography itself was of course agnostic, offering both “truth” and “magic” (e.g., spirit photographs aiming to show nonvisible emanations from beyond the grave). As the twentieth century dawned, artists themselves became “disruptors” of photography’s truth-claims, taking up the tool in an age of abstraction and revealing the powerful transitivity of the photograph through replication, “cutting,” and recombination in collage.
The waves of early adoption and transformation in photography and film both characterize and produce our present moment, as the visual training sets of GenAI are constituted primarily from digitized photographs and videos, whether historic or born digital. In theory, as in earlier historical moments, art made with the new technologies of GenAI can help the public understand how generative machine learning works in relation to their own inputs and data, and disrupt conventional views of reality.
The Perils and Promise of Generative AI in the Visual Arts
From an aesthetic point of view, there is a problem evident in the current state of imagery generated by the probability propositions of GenAI programs. Derivative, repetitive, and weak, the generated images can be characterized as “pastiche,” in which multiple styles from different epochs, regions, or authors are blended. This is a lowest-common-denominator approach that avoids offense yet achieves only the most banal effects.
Compare this pastiche aesthetic to the idea of edginess that true innovation brings. If we desire art forms that can open up new cognitive domains through shifts in visual practice, we should use the technoshock around GenAI (and disappointments in its banal results) to push for changes in the platforms to encourage human intervention and invention.
The troika of training sets, models, and interfaces must be opened to public use and radical research, whereby diverse communities have access and can make contributions to an expanding public good. Recommended approaches should foster artistic innovation and cultural correction by allowing artists and communities to enter GenAI’s “back end” – being able to see the sources of visual training, tweak the code, and contribute metadata from unique, community-based sources of corrective knowledge.
Such transparency and public access would also help rebalance the inferences used in GenAI, often toxic with accumulated bias. Marginalized populations all over the world see the effects of an image corpus that has originated primarily in the Northern hemisphere, is dominated by English-language tagging, and stems from historical epochs that may have skewed and dehumanized colonial subjects through words and pictures.
Humanities scholars will be critical in both surfacing and addressing the implicit biases in GenAI datasets, models, and interface design. Fortified by “homophily,” or preference algorithms that are known to segregate users and datasets across the internet based on originary racist criteria, the edge effects of GenAI are not “edgy” in their aesthetic forms. Expanding and pluralizing the visual archive, with rich metadata, is necessary to cultivate the kind of innovative art we desire.
Shaping GenAI for the Public Good
Ultimately, we argue that GenAI must be shaped to yield greater public good. It is time to examine more of the costs (including unacknowledged drain on the electrical grid) that undergird the benefits of this technology, and to curb unsustainable and undesirable patterns before they become embedded and entrenched.
Our recommendations focus on three key domains: datasets, models, and public interfaces. We envision a holistically designed, publicly owned, and accessible GenAI system with publicly available datasets, publicly accessible inferential models, and a universally accessible public interface that can serve as an exemplary global standard. Archives of prior training sets and models, as well as abandoned versions of the software, can open operations to tinkering and reinvention as well as thoughtful histories of technoshock—all prerequisites for truly innovative visual art.
By creating a single, globally accessible, energy efficient system developed through public-private collaborations, we can enrich archives, enhance metadata, ensure fair use, and foster the kind of innovative art that can stimulate reflection, produce desired cultural outcomes, and expand the capacities of these tools. The turbulent technoshock of our moment presents an opportunity to shape the future of GenAI in a way that allows “tinkering” by artists, encouraging conceptual innovations and new understandings that can benefit society as a whole.
