IOCSS Research Paper · Culture × AI Philosophy · 2026
Abstract
The integration of artificial intelligence into cultural production raises philosophical questions that extend far beyond concerns about job displacement or intellectual property. This paper argues that AI's entry into culture challenges three foundational concepts in the philosophy of culture: authorship, interpretation, and cultural memory. Drawing on hermeneutic philosophy, philosophy of art, and critical theory, we analyse how generative AI systems alter the conditions under which cultural meaning is produced, transmitted, and understood. We propose the concept of "synthetic media hermeneutics" — a framework for interpreting AI-generated and AI-assisted cultural objects that preserves the normative resources of humanist cultural criticism while accounting for AI's distinctive mode of cultural production. We argue against both techno-optimist celebrations of AI creativity and techno-pessimist dismissals of synthetic culture, contending instead that AI's cultural significance depends on the social and institutional contexts in which AI-generated content is embedded.
1. Introduction: Culture in the Age of Generative AI
Culture is how human beings make meaning. Through art, literature, music, ritual, and narrative, human communities create shared worlds of significance that give shape to individual and collective identity. Cultural production has always involved tools — from the sculptor's chisel to the printing press to the digital audio workstation — but these tools have historically functioned as extensions of human intentionality. The sculptor's chisel does not itself intend the form it shapes. The printing press does not select the text it reproduces. The digital audio workstation does not compose the music it processes.
Generative artificial intelligence — systems such as large language models, diffusion-based image generators, and music synthesis tools — represents a qualitatively different kind of cultural tool. These systems do not merely execute human intentions; they generate cultural objects through processes that are not, in any obvious sense, expressions of human intentionality. When GPT-4 writes a poem, Midjourney creates an image, or Suno composes a song, the output is not simply the execution of a human creative decision. It is the product of statistical processes operating on vast corpora of human-generated content, producing outputs that exhibit complex patterns of meaning without any human agent having formed the intention to produce them.
This paper examines what this development means for our understanding of culture. Section 2 analyses the concept of authorship and the philosophical challenges that AI-generated cultural objects pose to traditional accounts of artistic creation. Section 3 examines the hermeneutic problem: how do we interpret AI-generated texts and images? Section 4 analyses the relationship between AI and cultural memory, considering how AI systems trained on human cultural archives transform and transmit cultural heritage. Section 5 addresses the political economy of AI culture, examining who controls AI cultural infrastructure and whose cultural values it embeds. Section 6 proposes the framework of synthetic media hermeneutics. Section 7 concludes.
2. Authorship, Intentionality, and the Crisis of Creative Agency
The philosophy of authorship has long been contested terrain. The Romantic conception of authorship — the solitary genius expressing their inner vision — was challenged by structuralist and post-structuralist critics who argued that texts are produced by language, not by individual subjects. Roland Barthes' famous declaration of the "death of the author" was not a description of a technological change but a philosophical argument about the conditions of textual meaning: meaning is produced in the encounter between text and reader, not determined by authorial intention.
Generative AI introduces a new and philosophically distinct challenge to authorship. The question is no longer whether authorial intention determines meaning (Barthes' concern) but whether authorial intention is a necessary condition for cultural production at all. When a generative AI system produces a literary text, a visual image, or a musical composition, there is no author in any traditional sense — no human subject who formed the intention to create this particular object. The output is determined by the system's parameters, its training data, and the prompt provided by the human user.
Some philosophers have argued that this situation is not as radical as it appears: the human user who provides the prompt is the true author of AI-generated content, just as the director is the author of a film even though they do not personally operate every camera. On this view, AI is simply a more powerful and responsive tool than previous cultural technologies, and authorship shifts from the traditional creator to the AI prompter.
This response is too quick. The relationship between prompt and AI output is not analogous to the relationship between director's vision and cinematographer's execution. A skilled human collaborator understands the director's vision, can exercise judgment about how to realise it, and takes genuine responsibility for their contributions. A generative AI system does none of these things in any philosophically robust sense. Its outputs are not expressions of understanding or judgment; they are the products of statistical regularities in training data. The prompter does not direct the system toward a pre-existing vision; they explore a high-dimensional space of possible outputs by formulating queries in natural language.
The more honest philosophical position is to acknowledge that AI-generated cultural objects challenge the very concept of authorship in ways that require us to develop new frameworks rather than forcing new phenomena into old categories. The authorship of AI-generated content may need to be understood not as a property of individual agents but as a distributed achievement of human-machine systems operating within social and institutional contexts.
3. Hermeneutic Challenges of Synthetic Media
Hermeneutics is the philosophy of interpretation. The classical hermeneutic question — how do we interpret texts? — has been addressed by thinkers from Schleiermacher and Dilthey to Gadamer and Ricoeur, each developing frameworks for understanding how texts produce meaning and how readers access it. The hermeneutic tradition assumes, in most of its major formulations, that texts are produced by human authors who express meanings through culturally embedded linguistic acts, and that interpretation involves recovering or reconstructing those meanings through a dialogue between reader and text.
Generative AI disrupts these assumptions at every point. Consider the problem of intention. Gadamerian hermeneutics holds that interpretation involves entering into a "fusion of horizons" — a dialogue in which the reader's historical and cultural situation encounters the situation of the text's production. This fusion presupposes that the text has a situation of production — a historical, cultural, and intentional context in which it was created. An AI-generated text has no such context in any robust sense. It was produced by a statistical process trained on vast corpora, not by a human agent embedded in a specific historical situation. The question of whose horizon the reader is fusing with is genuinely unclear.
The hermeneutic problem is further complicated by the opacity of generative AI systems. A human author's meaning can, in principle, be contextualised through reference to their biography, other works, historical situation, and stated intentions. An AI system's "meaning" — insofar as it has any — is encoded in billions of parameters that no human can directly inspect or interpret. The output of an AI system is in a very strong sense orphaned from its conditions of production in a way that human-authored texts are not.
This has consequences for cultural criticism. The tools of literary and cultural criticism — close reading, authorial intentionalism, historical contextualisation, ideological analysis — presuppose that cultural objects are products of human agents embedded in social and historical contexts whose relationships to those contexts can be analysed. When we read a Dickens novel, we can ask what it tells us about Victorian society, about Dickens' own class position and psychological conflicts, and about the ideological work it performs. Can we ask analogous questions about an AI-generated novel? The object was produced by a system trained on human-generated texts; it embeds regularities present in that training corpus; it will be interpreted by human readers embedded in specific social situations. It has hermeneutic significance — but that significance requires new analytical frameworks to articulate.
4. AI and Cultural Memory: Archive, Simulation, and the Problem of Heritage
Culture is not only produced; it is transmitted. Cultural heritage — the accumulated creative and expressive achievements of human communities — is one of humanity's most valuable collective possessions. The transmission and preservation of cultural heritage has been transformed by digitisation, and generative AI represents the next stage of that transformation.
On one view, AI offers powerful tools for cultural preservation and democratisation. Machine learning systems can analyse, categorise, and make accessible vast archives of cultural material that would otherwise be inaccessible to most people. Natural language processing can translate texts across languages with unprecedented speed and quality. Image recognition can identify and contextualise works of art in ways that require years of specialist training for human experts. These capabilities could, in principle, greatly expand access to the cultural heritage of all humanity.
But generative AI's relationship to cultural heritage is more complex and troubling than this optimistic picture suggests. Large language and image models are trained on cultural archives without the consent of the human creators who produced the works in those archives. This raises obvious questions of intellectual property and exploitation, but also deeper questions about cultural appropriation. The cultural productions of marginalised communities — indigenous artists, minority language writers, communities whose cultural expressions are systematically underrepresented in mainstream archives — are absorbed into training data that produces outputs primarily optimised for the preferences of wealthy, English-speaking users. The cultural inheritance of all humanity is processed through systems designed to serve the interests of a subset of it.
There is also the problem of what we might call simulated heritage: AI systems capable of generating new content "in the style of" deceased artists, or of completing unfinished works, or of producing new works that mimic the conventions of historical cultural forms. These capabilities raise profound questions about cultural authenticity and the ethics of posthumous representation. When an AI system generates a poem in the style of Emily Dickinson, or a painting in the manner of Vermeer, it draws on human cultural heritage to produce simulations that may be indistinguishable from originals to most consumers. The boundary between heritage and simulation, between the authentic and the synthetic, becomes unstable in ways that challenge our understanding of cultural value.
5. The Political Economy of AI Cultural Production
The philosophical questions raised by AI cultural production do not arise in a social vacuum. They are embedded in a specific political economy in which a small number of technology companies — primarily based in the United States and China — control the infrastructure of AI cultural production. This concentration of control has profound implications for cultural diversity, democratic governance, and the distribution of cultural power.
The training data on which generative AI systems are built is largely composed of English-language content. This reflects both the composition of internet content and the resource constraints of AI development: training data must be large, clean, and easily accessible, and English-language content disproportionately satisfies these requirements. The consequence is that AI cultural systems encode and reproduce primarily the aesthetic preferences, cultural references, and ideological frameworks of English-language Western culture. When these systems are deployed globally, they constitute a form of cultural power: users worldwide are offered AI tools that reflect Western aesthetic norms as universal defaults.
The governance of AI cultural infrastructure is largely private and unaccountable. The decisions that determine what kinds of cultural content AI systems generate, what aesthetic standards they embody, what political and social perspectives they reflect, and what kinds of cultural expression they exclude are made by engineers and product managers at technology companies in Silicon Valley and Beijing, not by democratic publics or cultural communities. The cultural output of these systems — which is increasingly consumed by billions of people — is shaped by commercial imperatives and technical constraints rather than cultural values or democratic deliberation.
6. Synthetic Media Hermeneutics: A Framework
The philosophical challenges analysed in the preceding sections require a new framework for understanding and evaluating AI-generated cultural production. We propose the concept of "synthetic media hermeneutics" — a set of interpretive principles designed to account for AI's distinctive mode of cultural production while preserving the normative resources of humanist cultural criticism.
Synthetic media hermeneutics proceeds from three foundational commitments. First, AI-generated cultural objects are genuine cultural objects: they produce meaning effects in audiences, they engage with cultural traditions and conventions, and they have social and political significance. Dismissing them as "mere simulation" or "not really culture" is analytically unproductive and empirically inaccurate.
Second, the interpretation of AI-generated cultural objects requires attention to the conditions of their production: the training data, the architectural choices, the prompt, the institutional context, and the commercial and political interests of the systems' developers. These conditions do not determine the meaning of AI outputs, but they shape the space of meanings that those outputs can produce.
Third, the ethical evaluation of AI cultural production requires attending to questions of power, access, and cultural diversity. Who controls AI cultural infrastructure? Whose cultural values are embedded in AI systems? Who benefits from and who is harmed by the deployment of AI cultural tools? These are political as well as philosophical questions, and they require responses at the level of policy and institutional design as well as critical reflection.
7. Conclusion
Generative AI is not simply a new tool for cultural production; it is a challenge to the philosophical foundations of our understanding of culture. The concepts of authorship, interpretation, and cultural memory that have organised humanist thinking about culture were developed in a context where cultural production was, at its core, a human activity: an expression of human intentionality, embedded in human social life, and oriented toward human understanding.
AI cultural production does not simply extend this tradition; it disrupts its foundational assumptions. The framework of synthetic media hermeneutics we have proposed is an attempt to develop new analytical resources adequate to this disruption — resources that take seriously both AI's genuine cultural significance and the risks that AI poses to cultural diversity, democratic self-determination, and the distinctively human dimensions of cultural life.
The most important questions raised by AI and culture are not technical questions about what AI systems can produce. They are political and philosophical questions about what kind of culture we want to live in, who should control the infrastructure of cultural production, and how we can preserve the conditions under which human beings can make meaning together in ways that express rather than efface the diversity and complexity of human life.
References
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