Abstract

Knowledge workers increasingly produce documents, code, and analyses with assistance from generative artificial intelligence. A question that has received limited direct attention is how those workers describe their relationship to that output after the fact, and in particular how that description changes when the output is praised or criticized. This paper proposes that ownership of AIassisted work is not a fixed property of the work itself but a strategic claim that flexes with consequences. Drawing on attribution theory [1, 2], research on moral crumple zones [3], and recent findings on disclosure penalties for AI use [5, 7], the paper argues that AI assistance gives the worker a deniability asset: an option that is held quietly while outcomes are favorable and exercised, by attributing the work to the machine, when outcomes turn unfavorable. The paper formalizes this option structure, identifies five moderating conditions, states six testable propositions, and specifies an experimental design capable of measuring the effect together with the pattern of results the model predicts. Analysis is extended across four deployment scenarios spanning software engineering, professional writing, financial analysis, and academic authorship, showing how the predicted magnitude of the effect varies with verifiability and domain identity. The contribution is conceptual. The intent is to give researchers in human-computer interaction, organizational behavior, and AI governance a precise vocabulary and a measurable construct for a behavior that is currently discussed mostly through anecdote.

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