Building on the ongoing debate about AI art and copyright, [be sure to check out the “What is Art?” post first, so we have a common foundation — importantly, the idea that art is primarily about the resonance it creates with the subjective observer, regardless of whether it was created by a human or not.]
David Hume, a renowned 18th-century British philosopher, was one of the leading figures of empiricism — the view that all knowledge arises from sensory experience. Hume argued that everything in our minds — our ideas, feelings, and understanding — can ultimately be traced back to what we perceive through our senses or internal experience. For Hume, reason could not extend beyond the boundaries of experience; it is always rooted in, and limited by, what we encounter in the world.
A nice example from Sophie’s World, a Norwegian novel that I personally really like, illustrates this:
“…In Hume’s time, the idea that angels exist was widespread. By an angel, we understand a male figure with wings. Have you ever seen such a being, Sophie?” “No.” “But you have seen a male figure?” “That’s a stupid question.” “And you have also seen wings?” “Of course, but never on a human being.” “According to Hume, angels are a composite idea. They consist of two different experiences that, however, are not actually composed, but have only been coupled together in the human imagination…“
Therefore, an artist is also not a creator out of nothing, but a rearranger of experience. That means that art is the artful recombination of what we already feel and know.
We find various examples of this in history. Delacroix drew inspiration from Rubens and the Venetian Renaissance, favoring color and movement over precise outlines (see, for instance, Wikipedia). The earliest known version of the Romeo and Juliet story comes from Masuccio Salernitano’s Il Novellino (1476), with the tale of Mariotto and Ganozza (see, for instance, Wikipedia). Marcel Duchamp’s Fountain — a standard urinal placed in a gallery and signed with a pseudonym — challenged traditional notions of art (see Wikipedia). Even in pop music, Lady Gaga borrowed from Vittorio Monti’s Csárdás — itself based on a Hungarian folk dance — for the intro of her single Alejandro (see Classic Fm).
So we are standing on the shoulders of giants — a concept that dates back to the 12th century and, according to John of Salisbury, is attributed to Bernard of Chartres. Its most familiar and popular expression appears in a 1675 letter by Isaac Newton (see Wikipedia). So even this statement itself is reused and remixed. Every experience we have shapes our future actions and experiences. From an empiricist point of view, the very idea of “pure originality” is a myth.
So, what does this mean for the ongoing debate about AI and copyright violations? In an opinion shaped by the empiricism philosophy, AI systems do what we do: they examine publicly available data, gain experience, and use it to refine and improve themselves.
For example, just as a musician might listen to countless songs to develop their own style, an AI trained on publicly available music learns patterns, structures, and techniques.
Similarly, a painter might study masterpieces in museums, internalizing techniques, color palettes, and compositions, and then blend these impressions into their own original work — just as an AI trained on public visual datasets learns and recombines artistic styles. Or a writer might read hundreds of novels, essays, and poems, unconsciously absorbing narrative rhythms, styles, and ideas, which later reemerge in new combinations in their own writing — just as a language model, trained on publicly available text, weaves together new stories shaped by what it has read.
Therefore, as long as a work is publicly available for viewing (for example, accessible on the internet), it may be used for training an AI system. However, this freedom must be balanced with transparency: AI developers should maintain a publicly available reference database that clearly documents the sources of the training data — a task that is technically feasible. In addition, AI-generated works should be accompanied by a statement indicating that the total list of these sources influenced the creation of the generated piece. This goodwill approach would uphold the principles of fair use while ensuring that the origins of inspiration are acknowledged. Importantly, AI developers should not be required to financially compensate artists simply because their publicly available works contributed to the AI’s training. This principle reflects how creativity itself works: humans absorb and are influenced by countless works of art, often without consciously tracing every source of inspiration. In much the same way, AI models internalize patterns through exposure. Learning through observation — without direct copying — is a natural and essential process for both human and machine creativity. Artists do not pay every creator of the artworks they encounter.
Finally, the resulting artwork should be evaluated solely under existing copyright laws, without regard to whether a human or an AI created it.
If an artwork was generated in the style of a specific artist, such as Nobuyoshi Araki, it should be regarded not simply as a copy, but as part of a new artistic movement — for example, Arakiism. This framing both honors the original creator, recognizing their style as an invention worthy of its own lineage, and allows for the natural evolution of the style through new interpretations and transformations. Just as Impressionism or Cubism began with a few individuals and grew into broader movements, styles pioneered by individual artists can, through widespread engagement, become shared aesthetic languages. Viewing such works as contributions to an -ism rather than as imitations respects the dynamic, living nature of artistic creation and gives rightful credit to the originator.
In summary, from an empiricist perspective, human creativity is fundamentally a process of recombination — we reshape what we experience into something new. AI, trained on data, mirrors this same process. Therefore, AI should be permitted to learn from all publicly available data, provided that developers maintain a transparent, publicly accessible database documenting the sources. Copyright enforcement should continue as it did before the rise of AI technologies, focusing solely on the nature of the work itself, not on who or what created it. If an AI system user generates an artwork in the style of a specific artist, it should be recognized as part of the [Artist’s Name]ism art movement, in order to credit the original inventor of the style properly.