Katherine Elkins

AI development has a fiction dependency problem: models are built on massive corpora of modern fiction and desperately need more of it, yet they struggle to generate it. I term this the AI-Fiction Paradox and it is particularly startling because in machine learning, training data typically determines output quality. This paper offers a theoretically precise account of why fiction resists AI generation by identifying three distinct challenges for current architectures. First, fiction depends on what I call narrative causation, a form of plot logic where events must feel both surprising in the moment and retrospectively inevitable. This temporal paradox fundamentally conflicts with the forward-generation logic of transformer architectures. Second, I identify an informational revaluation challenge: fiction systematically violates the computational assumption that informational importance aligns with statistical salience, requiring readers and models alike to retrospectively reweight the significance of narrative details in ways that current attention mechanisms cannot perform. Third, drawing on over seven years of collaborative research on sentiment arcs, I argue that compelling fiction requires multi-scale emotional architecture, the orchestration of sentiment at word, sentence, scene, and arc levels simultaneously. Together, these three challenges explain both why AI companies have risked billion-dollar lawsuits for access to modern fiction and why that fiction remains so difficult to replicate. The analysis also raises urgent questions about what happens when these challenges are overcome. Fiction concentrates uniquely powerful cognitive and emotional patterns for modeling human behavior, and mastery of these patterns by AI systems would represent not just a creative achievement but a potent vehicle for human manipulation at scale.

With the rise of individual and collaborative networks of autonomous agents, AI is deployed in more key reasoning and decision-making roles. For this reason, ethics-based audits play a pivotal role in the rapidly growing fields of AI safety and regulation. This paper undertakes an ethics-based audit to probe the 8 leading commercial and open-source Large Language Models including GPT-4. We assess explicability and trustworthiness by a) establishing how well different models engage in moral reasoning and b) comparing normative values underlying models as ethical frameworks. We employ an experimental, evidence-based approach that challenges the models with ethical dilemmas in order to probe human-AI alignment. The ethical scenarios are designed to require a decision in which the particulars of the situation may or may not necessitate deviating from normative ethical principles. A sophisticated ethical framework was consistently elicited in one model, GPT-4. Nonetheless, troubling findings include underlying normative frameworks with clear bias towards particular cultural norms. Many models also exhibit disturbing authoritarian tendencies.

We know that Kafka was fascinated by pictures,1 and they play a prominent role in his work. In Der Prozeß, visual art is elevated to a privileged position when K. visits a painter for advice about his trial.2 Many scholars have explored Kafka's interest in visual media,3 but none has described in detail the picture lessons that take place in the novel. This may be because many take their cue from the end, when K. wonders if people will say he's learned nothing. But does K. really learn nothing from pictures? In fact, there are many picture lessons in the novel—from the sketch K. examines on the judge's desk to the painting that K scrutinizes in the cathedral.

Unlike most fiction writers, Proust was trained in philosophy. In fact, he even considered writing a philosophical treatise instead of the novel we know so well. This hesitation about what form his writing should take still haunts his final choice of a novel, which is both philosophical, and yet, not philosophy. Take your pick of philosophers, from Plato to Nietzsche, and you can easily find an essay or even a book arguing that this particular philosopher most applies to Proust. But as one plunges into the narrative that he finally wrote, one is struck by the fact that In Search of Lost Time feels nothing like what we often call a philosophical novel, or even, a novel of ideas. Instead, philosophical reflection lies in the shadows of his fictional world, a sort of parallel life that can be found in the underweave.

Drawing from classical philosophical discourse, including Plato's reflections on knowledge and memory, this essay navigates the transition from traditional memory repositories to modern insights from Proust and Wordsworth to digital platforms. With the rise of AI, particularly Large Language Models (LLMs), new generative technologies challenge our traditional notions of memory and knowledge. Integrating Ong's concept of "secondary orality," the essay explores how technology, especially AI, might be ushering in a new era reminiscent of oral traditions, yet distinct in its digital nature. How do these AI models, which mimic human cognition, influence our understanding of knowledge and wisdom? And as these technologies become more integrated into our daily lives, what does it mean for the future of human knowledge?

Why, in Plato's Symposium, does Socrates claim he learns about love from Diotima? Voicing a philosophical account of love through the figure of Diotima disguises the real female authority in the text who remains unnamed: Sappho. Alcibiades's speech echoes elements of Sappho's poetic descriptions of love, thus enacting a dialogue between a philosophical account and an older, lyric one. Naming the lyric in Plato's dialogue makes visible a continuing debate about love that has important ramifications for understanding aesthetic experience.