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This research note explores the legal and ethical implications of ownership and liability in AI-assisted software development, specifically addressing how responsibility is allocated when generated code fails. It proposes a framework for quality engineering that evaluates both the generated implementations and the underlying production processes without relying on new empirical data. The discussion centers on shifting from traditional authorship models to service-based obligations where acceptance criteria are defined by required outcomes rather than code origin.
Sources coveringarXiv, LessWrong, The Verge, The Quantum Insider
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

It Is Not My Code Anymore

arXiv2 min read

arXiv:2609.09022v1 Announce Type: cross Abstract: AI-assisted programming raises distinct questions about who produces code, who feels ownership of it, and who is responsible when it fails. This research note examines these distinctions through a hypothetical enrollment failure and a selective reading of the literature. Identifying the producer of a defective expression does not, by itself, determine the duties of reviewers, release decision-makers, or service operators. Collective ownership likewise leaves those duties to be specified. The discussion then considers how quality engineering can evaluate both generated implementations and the processes that produce them. Acceptance criteria should be justified by the required service outcome, with component checks contributing evidence toward that outcome. This perspective also permits comparison with systems that perform a task without generating a separate program for it. Such substitution would change the object of authorship while leaving the service obligation intact. The note reports no new empirical results; it proposes distinctions and evaluation questions for AI-assisted software production.

Abstract:AI-assisted programming raises distinct questions about who produces code, who feels ownership of it, and who is responsible when it fails. This research note examines these distinctions through a hypothetical enrollment failure and a selective reading of the literature. Identifying the producer of a defective expression does not, by itself, determine the duties of reviewers, release decision-makers, or service operators. Collective ownership likewise leaves those duties to be specified. The discussion then considers how quality engineering can evaluate both generated implementations and the processes that produce them. Acceptance criteria should be justified by the required service outcome, with component checks contributing evidence toward that outcome. This perspective also permits comparison with systems that perform a task without generating a separate program for it. Such substitution would change the object of authorship while leaving the service obligation intact. The note reports no new empirical results; it proposes distinctions and evaluation questions for AI-assisted software production.

7 pages, 2 figures, 1 table. Research note

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

arXiv:2609.09022 [cs.SE]

(or arXiv:2609.09022v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2609.09022 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 259 words

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