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.
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.
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...
arXiv:2607. 12755v1 Announce Type: cross Abstract: AI-enabled systems are seeing increasing deployment across numerous domains, with many being "black boxes" with respect to core functions and capabilities.
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:2606. 30652v1 Announce Type: cross Abstract: Transparency is increasingly mandated for public-sector AI systems, with organisations required to publish statements describing their AI use and oversight arrangements.
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...
The paper investigates when an interpretation in generative AI is considered established, arguing that passing local factual checks is insufficient. It introduces three concepts—interpretive appearance, evaluation contract, and standing substitution—to analyze how interpretations gain recognition within sociotechnical processes. The authors propose delayed closure as a practice to keep recognized interpretations revisable and outline five public requirements for transparency, evidence, failure handling, contract revision, and responsibility.
The paper examines how large language model (LLM) outputs are increasingly used in contexts that demand justified interpretations, such as law, education, policy analysis, and public moral debate. It identifies a recurring failure—interpretive misplacement—where model-generated readings are treated as settled meanings without explicit interpretive frames, provenance, or defensible alternatives, leading to accountability loss. Drawing on philosophical hermeneutics, the author proposes design principles for human‑AI co‑interpretation, reorganizes existing LLM techniques into hermeneutically responsible patterns, and discusses implications for legal practice, education, scholarship, and public discourse, while framing digital hermeneutics as a literacy for critically engaging with AI‑mediated texts.
AI-enabled systems are seeing increasing deployment across numerous domains, with many being "black boxes" with respect to core functions and capabilities. I.
The paper titled "Lost in Translation: How Universal Ethical Values Fail to Translate Across Global Contexts" examines how AI ethics frameworks that assume values such as fairness, transparency, and accountability are universal actually differ in practice. By interviewing 14 experts from 10 countries, the authors find that these values are reinterpreted to fit local moral logics—privacy becomes collective, transparency becomes trust‑building, and fairness becomes equity in access—revealing translation gaps between global frameworks and local practices. The study proposes plural governance pathways that redistribute epistemic authority and treat ethical negotiation as an ongoing, context‑sensitive process.
arXiv:2606. 11218v1 Announce Type: cross Abstract: Ethical deliberation is often misunderstood as a search for single right or wrong answers, creating difficulties for non-ethically trained personnel who must address ethically laden challenges.