epistemology

This paper identifies a non-composition principle in AI benchmark evaluation: support for adjacent projections does not automatically warrant their composition. It proposes a projectibility audit to diagnose unsupported joins in benchmark-to-use arguments, with a legal-research case study and simulations.

A preprint arguing that AI can map known biology but cannot perform 'kind formation'—the minting of new variables and constraints—which requires the embodied engagement of human scientists. The authors propose a curriculum to train scientists as 'detectors of the unparameterized' to complement AI in biological discovery.

This position paper argues that ground truth datasets in machine learning are not objective truths but human constructions shaped by choices, and advocates for articulating these choices to improve reliability, transparency, and accountability.

This paper proposes an adversarial social epistemology framework for analyzing trust, deception, and inference chains in communicative landscapes involving humans and large language models, and outlines mechanisms for auditing trust breaches.

This perspective paper develops a conceptual and methodological framework for evaluating evidence-licensed claims in AI-assisted research, emphasizing calibration as a mechanism for managing scientific assertion rights and distinguishing between different AI research routes.

This paper argues that probability theory is a historically evolving form of rationality, tracing its development from combinatorial games to Bayesian inference and contrasting it with fuzzy logic and deep learning.

This article explores model collapse not as a technical bug but as an epistemic problem: when an AI model's outputs become its own inputs, the model's representation of reality gradually flattens into a self-referential average, raising questions about how we distinguish a model that models the world from one that models only itself.