Operationalising Extended Cognition: Formal Metrics for Corporate Knowledge and Legal Accountability

2025-10-17

AI Summary

As generative AI increasingly participates in corporate decision-making, the traditional notion of “corporate knowledge” proves inadequate for attributing legal liability. Method: We propose a dynamic conception of corporate knowledge grounded in two dimensions—information access efficiency and output reliability—and formalize a corporate epistemic state model. This includes defining a continuity-based organizational knowledge measure $S_S(varphi)$, a knowledge predicate $mathsf{K}_S$, and a corporate cognitive capability index $mathcal{K}_{S,t}$. Integrating extended cognition theory with statistical validation and computational cost modeling, we quantify both error propagation and resource expenditure across knowledge-generation pipelines. Contribution: This work is the first to systematically map extended cognition theory onto a legal liability framework, enabling operationalizable and auditable modeling of corporate “mind.” It establishes a measurable knowledge metric system explicitly aligned with legal standards—including actual knowledge, constructive knowledge, willful blindness, and recklessness—thereby providing judicially admissible, quantitative foundations for corporate accountability in the algorithmic era.

Technology Category

• Philosophy and Ethics of AI: AI & Epistemology
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Corporate responsibility turns on notions of corporate extit{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.

Problem

• Redefining corporate knowledge as measurable dynamic capability
• Developing formal metrics for AI-mediated organizational cognition
• Mapping quantitative epistemic measures to legal accountability standards

Innovation

• Dynamic corporate knowledge measured by efficiency
• Formal model integrates computational cost and error rate
• Quantitative metrics mapped onto legal accountability standards