Operationalising Extended Cognition: Formal Metrics for Corporate Knowledge and Legal Accountability
This work addresses the problem of legal accountability for corporations using AI, offering a novel framework to make corporate knowledge measurable and justiciable, though it is incremental in applying extended cognition theory to legal contexts.
The paper tackles the challenge of attributing corporate knowledge and legal accountability in the age of AI-driven decision-making by proposing a formal model that defines corporate knowledge as a dynamic capability, measured through metrics like computational cost and error rates, and maps these to legal standards such as actual knowledge and recklessness.
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.