{"ID":2854298,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16193","arxiv_id":"2510.16193","title":"Operationalising Extended Cognition: Formal Metrics for Corporate Knowledge and Legal Accountability","abstract":"Corporate responsibility turns on notions of corporate \\textit{mens rea}, traditionally imputed from human agents. Yet these assumptions are under challenge as generative AI increasingly mediates enterprise decision-making. Building on the theory of extended cognition, we argue that in response corporate knowledge may be redefined as a dynamic capability, measurable by the efficiency of its information-access procedures and the validated reliability of their outputs. We develop a formal model that captures epistemic states of corporations deploying sophisticated AI or information systems, introducing a continuous organisational knowledge metric $S_S(\\varphi)$ which integrates a pipeline's computational cost and its statistically validated error rate. We derive a thresholded knowledge predicate $\\mathsf{K}_S$ to impute knowledge and a firm-wide epistemic capacity index $\\mathcal{K}_{S,t}$ to measure overall capability. We then operationally map these quantitative metrics onto the legal standards of actual knowledge, constructive knowledge, wilful blindness, and recklessness. Our work provides a pathway towards creating measurable and justiciable audit artefacts, that render the corporate mind tractable and accountable in the algorithmic age.","short_abstract":"Corporate responsibility turns on notions of corporate \\textit{mens rea}, traditionally imputed from human agents. Yet these assumptions are under challenge as generative AI increasingly mediates enterprise decision-making. Building on the theory of extended cognition, we argue that in response corporate knowledge may...","url_abs":"https://arxiv.org/abs/2510.16193","url_pdf":"https://arxiv.org/pdf/2510.16193v2","authors":"[\"Elija Perrier\"]","published":"2025-10-17T20:03:57Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}