Generative AI&Two Forms of Decoupling

2026-09-26

AI Summary

This study addresses how generative AI decouples text production from human activity, thereby undermining its evidentiary, inducive, and constitutive value. Integrating jurisprudential analysis, conceptual framework construction, and institutional design theory, this work reveals the internal mechanisms driving a “secondary decoupling” of textual functions caused by AI. It further demonstrates that existing remedial measures tend to prioritize high-visibility functions while marginalizing vulnerable stakeholders. To address this, the research innovatively proposes a taxonomic framework for textual value, advocating for the reconstruction of institutional practices through explicit deliberation. Ultimately, these findings provide a robust theoretical foundation for institutions seeking to recalibrate norms governing text usage, offering critical guidance for safeguarding the functional rights and interests of marginalized groups.

Abstract

Textual artifacts are sometimes valued not only for the words on the page, but for the human activity involved in producing them. The effort invested in a carefully tailored email can signal genuine interest; composing an apology might involve attending to another person's hurt and deciding how to respond; and requiring a judge to give written reasons may induce more careful deliberation. AI models create what we call a decoupling problem: they make it possible to produce text without undergoing the relevant human activity, severing its connection to values traditionally sustained by that activity. This Article develops a framework for understanding what decoupling puts at stake, and how institutions that use text to make consequential decisions may reshape their practices in response. First, it offers a taxonomy of the grounds for valuing the production process, distinguishing whether that process matters for what it evidences, induces, or helps constitute. Second, it argues that text can function as a kind of boundary object, allowing institutions to rely on the same artifact without resolving disagreements about why the practice is valuable. AI can separate functions previously served by the same practice - a second-order decoupling that brings unresolved questions about the practice's purposes into view. Third, it argues that institutional efforts to repair decoupling may recover some functions without preserving others. Such responses are likely to favor functions that are more legible, whose loss demands immediate attention, or whose stakeholders have greater influence. It therefore calls for more explicit deliberation about which particular functions of a practice to preserve, with meaningful representation for those whose interests might otherwise be overlooked.

Problem

• Generative AI
• Decoupling
• Textual artifacts
• Institutional decision-making
• Boundary object

Innovation

• Decoupling Problem
• Boundary Object
• Generative AI
• Taxonomy of Valuation
• Institutional Repair