AAP 2026 CONFERENCE

When existential and religious choices are made under uncertainty, complexity and entanglement are likely to follow (De Cruz, 2021, p. 2). This is especially true at the intersection of generative AI (GAI), philosophy, neuroscience, and theology. If, as some argue, AI has become a new kind of entity — an 'autosapiens' that is adaptive (it learns), amiable (it befriends), and arcane (it mystifies) — then the question of how we encounter and respond to it becomes urgent (Heimans & Timms, 2024). This commentary uses the concept of kairos — with its classical and theological resonances — to argue that philosophy, neuroscience, and theology not only share common ground in addressing this challenge, but that their genuine dialogue may yield important breakthroughs in understanding what it means to be human in an age of intelligent machines.
At the convergence of philosophy, neuroscience, and theology, this paper argues that the rise of generative AI constitutes a self-renewing kairos that calls for urgent interdisciplinary dialogue. As AI systems such as Claude increasingly occupy neurological and meaning-making roles once associated with gods and sages, sentience—the felt, conscious awareness that Claude lacks but humans possess—emerges as the decisive hinge of the entire human–AI encounter.

Much of the recent academic debate in the philosophy of AI revolves around a deceptively simple question: are AI tools only as good as their datasets? Existing discussions, however, tend to focus on improving the quality or quantity of training data, thereby underestimating a more subtle issue: how do AI agents handle outlier cases? This paper examines the ‘majoritarian drift’ – the tendency of AI agents to privilege positions with the largest training footprints, systematically marginalising underrepresented perspectives and edge cases. I approach this problem through the lens of exceptionalism, understood as a condition in which a phenomenon is sufficiently unusual to demand treatment outside standard frameworks. The paper proceeds in two parts. First, I demonstrate that majoritarian drift in ethical reasoning disproportionately favours utilitarian and aggregative approaches, while disadvantaging particularist, casuistic, and minority-tradition ethics. Second, I analyse analogous distortions in logical reasoning, focusing on how majoritarian drift impairs AI judgment in cases involving exceptions to general rules, with particular attention to legal clauses. The paper argues that exceptionalism reveals a structural limitation in how current AI architectures process normative and logical complexity.

Philosophers endorsing Computational Functionalism (CF) have argued we should afford AI systems moral consideration in virtue of their possessing (or possibly possessing) conscious states. I argue we have no good reason to think CF is true, and good reason to think it isn’t. I distinguish three versions of Computational Functionalism and give arguments against each. Identity CF says the property of a physical system implementing the right computation is identical to the property of physical system being in a conscious state. I show that on plausible assumptions about computational implementation we have a straightforward deductive argument against Identity CF. I then consider more popular versions of CF which say that the property of a physical system implementing the right computation is either ‘sufficient for’, or ‘necessary and sufficient for’ (rather than identical to), a physical system being in a conscious state. Responding to Chalmer’s dancing and fading qualia arguments, I argue we have no good reason to think that a physical system could be conscious in virtue of implementing a computation, and the idea ought to strike us as a bizarre and implausible.

It has recently been suggested that large language models (LLMs) fine-tuned on the corpus of text from palliative care patients could be used to alleviate their distress by completing projects or relationships that would otherwise be cut short by their deaths. For example, a fine-tuned LLM could be used to complete the novel of a dying author. I contest the alleged benefits of this technology by drawing on the philosophy of Martin Heidegger. Heidegger claims that our anticipation of death is significant because of its ability to induce anxiety, which he characterises as a collapse in the meaningfulness of our self-interpretations. This experience is valuable because it enables us to live authentically, that is to say, in a way that understands that we are not necessarily defined by any of our meaning-giving self-interpretations. I argue that fine-tuned LLMs would disarm death of anxiety and the benefits of authenticity, above all the ability to live with greater flexibility and openness to the present. After considering the potential benefits that fine-tuned LLMs may nonetheless bring to palliative care, I conclude that they should not replace the work of human therapists capable of guiding the dying through these intense existential feelings. 

Talk of “agentic AI” can illuminate real changes in technical delegation, but it can also move agency-talk toward artificial systems while human and institutional actors recede from view. This paper argues that responsible AI governance requires neither machine personhood nor metaphysical quietism, but fitting individuation: naming AI systems enough to govern, contest, authorize, and repair their uses without personifying them beyond warrant. On this account, AI systems are not autonomous moral agents, but socio-technical deployments through which judgment, authority, risk, and responsibility flow. The central question is therefore not simply “Is the AI an agent?” but “Where must judgment, contestability, and answerability be located for this deployment to remain governable?” I propose three adequacy tests: identity credentials sufficient for governance, delegation-with-answerability, and responsibility-flow mapping. These tests distinguish legitimate technical delegation from agency laundering, explanation theatre, and nominal human oversight. The result is a modest metaphysical account of AI: thick enough to locate responsibility, light enough to avoid machine mystification, and practical enough to guide institutional governance.

Many think that we should respect humans and not AIs. This paper shows that this approach runs into trouble in “ambiguous spaces,” where we can’t tell whether someone is an AI. We can either extend respect to ambiguous agents, or withhold respect from them. Either approach comes with significant costs. We call this dilemma the contagion of disrespect. Extending respect ties our hands, and incentivizes people to deploy ambiguous AIs against us. Withholding respect risks blocking some humans from respect, and risks creating spirals of disrespect.

Critics rightfully identify that AI models are biased against marginalised groups. These biases deteriorate our shared hermeneutical resources—the narratives, frameworks, and concepts that structure how we understand the world and ourselves—by reflecting and exacerbating existing oppressive narratives. However, this is not the only way that AI models are sources of hermeneutical impoverishment. I propose that AI models warp our hermeneutical resources, not only by reinforcing existing problematic representations of identity groups, but by changing how these groups are represented. That is, AI models are conceptual engineers, capable of revising our social concepts.
When certain deep machine learning models perform predictions, they construct social concepts. Crucially, these algorithmic concepts differ from their human-constructed counterparts due to unavoidable trade-offs in model development. In constructing revised algorithmic concepts, AI models act as conceptual engineers. Once introduced, algorithmic concepts can take the place of our own concepts. Through these hermeneutical changes, AI models can also make a difference to our underlying social ontology: in redefining how we think of ourselves, they can redefine who we are. Finally, I offer upshots of attending to AI models as novel sources of epistemic and ontological harm.