The paper examines how AI involvement in human collaboration can lead to unowned judgment, where decisions shaped by AI lack a clear accountable human or institution. Through cases of AI-assisted peer review and concealed AI use in creative work, it shows that contribution dissolution weakens responsibility and that fear of losing credit can deter disclosure. The authors argue for clearer distinctions of AI roles, identification of judgments needing human ownership, and conditions that allow disclosure without penalty, aiming to make AI-shaped contributions discussable, creditable, contestable, and repairable.
Communities often respond to potentially AI-assisted work by asking three questions: Was AI used? Was that use disclosed? Can hidden use be detected? These questions place AI use itself at the center...
The study investigates when work done with AI feels like one's own, using a qualitative survey where participants described tasks that felt owned versus not owned. Findings show that ownership depends on the collaboration process: people feel ownership when they lead, iterate, or rewrite, but disown work when merely approving AI suggestions. Ownership also extends to tasks where people set the vision but rely on AI for execution, yet loss of personal voice and lack of comprehension erode ownership, and willingness to disclose AI use is driven more by community norms than by pride.
The paper examines AI disclosure policies in top computer science venues, finding them to be highly under‑specified. A survey of 109 researchers shows that disclosures are deemed most necessary for research design tasks and when human involvement is low, and it compiles researchers’ expectations for disclosure content. Analysis of 13,867 disclosure statements from EMNLP 2025 and ICLR 2026 reveals a significant mismatch between these expectations and actual practice, such as frequent disclosure of writing assistance despite it being considered less necessary.
arXiv:2511. 08639v4 Announce Type: replace-cross Abstract: Existing AI disclosure mandates in scholarship require that AI assistance be reported but leave transparency philosophically unspecified: they fix the duty without explaining what the duty serves.
As generative AI tools find increasing use in research workflows, ongoing debates on their impact, appropriateness and responsible use have led policymakers to enact policies to disclose AI use at mul...
arXiv:2606. 11116v1 Announce Type: cross Abstract: As newsrooms integrate generative AI, journalists face a disclosure challenge: how to communicate AI involvement in ways that maintain reader trust.
arXiv:2606. 00621v1 Announce Type: cross Abstract: Generative artificial intelligence has fundamentally changed how content is now produced.
arXiv:2606. 10159v1 Announce Type: cross Abstract: AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage.
arXiv:2607. 15027v1 Announce Type: new Abstract: The integration of AI-driven systems in creative work has sparked debates among artists and legal communities about notions of ownership.
The paper introduces Provenance Density, an interface that visualizes the density of verified claims within a text to counter the Fluency Trap—where users mistake fluent AI-generated hallucinations for truth. In a study with 81 participants, the interface significantly improved users’ ability to distinguish true from fabricated content, while no signal led to no discernment. A technical audit of 200 samples revealed that retrieval density alone is insufficient, and that the Consistency Veto provides most of the discriminative power for dynamic queries.
arXiv:2607. 25637v1 Announce Type: cross Abstract: F(AI)2R is FAIR research with AI in the loop, twice: an AI-assisted authoring pass and a machine-readable audit pass over every artefact.
arXiv:2608. 19511v1 Announce Type: new Abstract: Symposium is a formal framework and practical implementation to record the operation of AI agents deployed by small scientific research communities.