ai & art

The rapid advancement of artificial intelligence has been a broad and popular topic in the past few months, and most tend to have views predicting it will lead to an idyllic utopia or that it will bring the end of humanity. But speculation about where AI technology is going, while important, can also drown out important conversations about how we should be handling the AI technologies available today and in the past. Generative AI, for example, which can create content including text, images, audio, and video, and has been a large discussion in the context of art and has already made significant impacts in the community. Generative AI tools are doing things that even a few years ago we never thought would be possible, raising a lot of fundamental questions about the creative process and the human’s role in creative production. However, the complexity of black-box AI systems can make it hard for researchers and the broader public to understand what’s happening under the hood, and what the impacts of these tools on society will be. In recognition of this, it is imperative that we understand how perceptions of the generative process affect attitudes toward the outputs and authors/creators of it. We must also consider how we can design the interfaces and systems so that they are more transparent about the generative process behind them and avoid misleading interpretations, changing the general opinion on AI’s role in art.

With aesthetics and culture, we can consider how past art technologies can inform how we think about AI. For example, when photography was invented, some painters said it was “the end of art.” But instead, it ended up being its own medium and eventually liberated painting from realism, giving rise to Impressionism and the modern art movement. Generative AI, similarly, can be considered as its own medium, and the nature of art may evolve with it, raising questions about its usage and artists will be able to potentially express intent and style through this new tool.

Artificial intelligence based image generators have raised the prospect of the emergence not just of a radically new art movement but of the end of art itself—and especially of the artist as an individual human genius. Their arrival has also posed a fundamental philosophical question: Can machines be truly creative? Salvador Dalí's path as an artist can potentially answer some of these fundamental questions.

One precursor to Dalí's artistic shift was his adoption of a “style transfer” through which he painted first like a Renaissance master and then like Picasso. This same imitative method is presently employed in projects such as Microsoft's The Next Rembrandt. Throughout his career, Dalí mined existing repositories of art history resources and used what he found there as inspiration to produce strikingly new artifacts. With this approach, Dalí put into practice an understanding of creativity as “the ability to come up with ideas or artefacts that are new, surprising, and valuable” that was codified nearly a century later by computer scientist Margaret Boden in her book Creativity and Art. Boden's definition is novel because it allows for the possibility that creativity may be exercised by both humans and machines, thus opening the door to defining art in a way that exceeds its humanist legacy.

But even after the creation of the art, how should we think about the qualities of these new artifacts deemed “art,” and how or by who can this decision be made? Art, the “expression or application of human creative skill in visual form” is not merely the production of aesthetically pleasing or meaningful beautiful artifacts; especially as the very notion of beauty is contextual and not widespread. In addition, art has recipients for who the pieces have various meanings that matter, so any perceived value is dependent on the audience. In some iterations, art also has financial value, is treated as an investment, and functions as part of a global exchange of capital, which I will get into later.

AI can produce images that do not exist in the world, and this trait has attracted the attention of artists working across media, including visual arts, music, and writing. Such work draws upon the insights and philosophical contributions of the Romantic era. For Romantics, the artist's calling was to “disimprison” art from the constraints of convention, to reveal the inner essence of the world, and to restore unity through the medium of creative expression. No longer constrained by the world, art and beauty for the Romantics transcended existing realities. AI-generated art can be seen as a contemporary manifestation of these Romantic ideals: a kind of art that breaks free from the confines of the material world, exploring new realms of visual and conceptual possibilities. AI-generated art also has the potential to expand the horizons of artistic expression and accessibility, making art more inclusive and diverse. This inclusivity has the potential to democratize the creative process, as AI tools become increasingly accessible to a wide range of people, regardless of their artistic background or training. It empowers individuals to explore their creative potential and engage in artistic expression that they may not have had the ability to do earlier. The blurring of lines between amateur and professional artists can enrich the artistic landscape, injecting fresh perspectives. However, for labor economics and creative work, the idea is these generative AI systems can accelerate the creative process in many ways, but they can also remove the ideation process that starts with a blank slate. Moving forward, there will be a need to think carefully about how these tools are used to complement people’s work instead of replacing it.

While AI-generated art holds promise, it also comes with its share of controversies and challenges. One significant concern is the potential for AI to perpetuate bias and stereotypes present in its training data. If AI systems are trained on data that reflects existing societal biases, they may reproduce those biases in their generated art. This raises important ethical questions about the responsibility of AI creators to mitigate bias and ensure that AI-generated content is inclusive and respectful of diverse perspectives.

Another large and well-known issue with AI’s role in art lands in the realm of artist rights to their work. Issues around ownership and credit are tricky because we need copyright law that benefits creators, users, and society at large. Today’s copyright laws cannot adequately defend and give rights to artists explicitly in cases when these systems are training on their styles and work without their permission. Moreover, the impact of AI on the art market is a topic of ongoing discussion. As AI-generated art gains recognition and value, questions arise about its place in the art market ecosystem. The art market has traditionally relied on the uniqueness and scarcity of physical artworks. AI-generated art, being digital and reproducible, challenges these traditional practices that allow for the current monetization of art pieces. There are many challenges regarding how to assign monetary value to AI-generated pieces, and how to authenticate them. These questions require careful consideration as the art market adapts to the digital age.

In conclusion, AI in art represents a transformative force that challenges traditional notions of creativity, authorship, and aesthetics. It raises fundamental questions about the role of intentionality in art, the democratization of creativity, and the evolving practices of critics and curators. While AI-generated art has the potential to expand the horizons of artistic expression and accessibility, it also presents ethical and legal challenges related to bias, ownership, and the art market. As AI technology continues to advance, the dialogue around AI in art will evolve, and society will need to grapple with these complex issues to shape a future where AI and human creativity can seamlessly coexist.