Generative AI raises issues by separating text from human intent
Generative AI & Two Forms of Decoupling
Computers and Society
Summary
Sometimes, the value of writing comes from the human effort behind it, like showing care in an apology or thoughtfulness in a judge's decision. The authors explain that AI can produce text without that human involvement, breaking the link to those values. They offer a way to understand which parts of the human process matter and show how organizations might struggle to keep those values when using AI-written text. They also warn that when institutions try to fix these issues, they might only preserve some values and not others, so careful choices are needed.
What this means in practice
- •For legal professionals: Adjust policies to handle AI-created text that might lack human deliberation in legal reasoning.
- •For organizational managers: Develop guidelines for when AI-written text can replace human-generated documents without losing critical trust signals.
A position paper. It proposes an approach and reports no results.
Authors
Shira Gur-Arieh, Sina Fazelpour
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.