Philosophy as Cognitive Assay: Measuring the Delegation Legitimacy Boundary in AI-Assisted Knowledge Work

Kengo Tomita
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Kengo Tomita: Shimizu Corporation

No e9qw5_v1, SocArXiv from Center for Open Science

Abstract: This article operationalizes the concept of a delegation legitimacy boundary — the structural line along which human judgment can and cannot be legitimately delegated to artificial intelligence — and proposes a minimal scoring protocol for locating it within any knowledge-work task. Building on the Mekiki framework (Tomita 2026), which distinguished specification — the task-defining substrate of domain expertise — from externalization cost — the technical barrier that AI selectively removes — the present article derives a further decomposition within specification itself. Drawing on case-study evidence in which recorded specification judgments contain separable factual components (Sein-type: what the data structure affords, how users will perceive a display) and value-laden components (Sollen-type: what information ought to be excluded, which priority ranking is appropriate), the article grounds this distinction in Hume’s is–ought separation and Kant’s Sein/Sollen architecture, and redeploys it as the axis of a cognitive assay — a measurement system in which philosophical categories serve not as normative prescriptions but as diagnostic coordinates. The resulting five-step scoring protocol assigns Sein-type components to AI evaluation and Sollen-type components to domain-expert evaluation; this asymmetry is not a design choice but a structural consequence of the boundary itself, which holds as long as legitimacy over value judgments remains institutionally human-attributed. Most individual judgments are hybrid, carrying both components in varying ratios; the protocol therefore yields ratio profiles rather than binary classifications. As a first application, the article re-describes the four processes of Nonaka and Takeuchi’s Socialization–Externalization–Combination–Internalization (SECI) model through the assay, deriving as an analytic consequence the finding that AI acceleration of Externalization and Combination shifts the effective rate-limiting stage to Socialization and Internalization — both human-limited cognitive and social processes that cannot be accelerated by AI investment alone.

Date: 2026-05-15
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:e9qw5_v1

DOI: 10.31219/osf.io/e9qw5_v1

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