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.
By Arati Mohapatra, Danish Pruthi
arXiv:2506. 03218v2 Announce Type: replace-cross Abstract: The EU has become one of the vanguards in regulating the digital age.
By Alina Wernick, Kristof Meding
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.
By Pooja Prajod
The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content provenance and AI involvement. But the effects of such disclosures remain uncertain.
arXiv:2609.39818v1 Announce Type: new
Abstract: In many high-stakes settings, human decision-makers can acquire support information before making a decision. However, acquiring information is costly,...
By Carlotta Giacchetta, Alessando Bogani, Cesare Barbera, Giovanni De Toni, Michele Caprio, Andrea Pugnana, Andrea Passerini
arXiv:2608. 11794v1 Announce Type: cross Abstract: The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content provenance and AI involvement.
By Adrian Rauchfleisch, Andreas Jungherr
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.
By Michele Loi
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.
By Hengzhi Ye
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 paper investigates how different stakeholders—end‑users, AI builders, and policymakers—define and experience transparency in generative AI. It finds that while all groups agree on core goals of accuracy and privacy, end‑users perceive larger gaps between desired and available transparency features, with only 28% feeling well served compared to 53% of builders and 49% of policymakers. The study also shows that transparency is context‑dependent, shifting priorities based on task and role, and calls for pluralistic transparency infrastructures rather than a one‑size‑fits‑all approach.
By Genevieve Smith, Sahiba Chopra, Natalia Luka, Min Kyung Lee
The study examines whether annual reports can reveal how companies disclose AI-related risks and responses. Using a two-stage classification pipeline on 9,821 reports from 1,362 UK listed firms (2020‑2026), the authors find that mentions of AI risk rose from 2.8% to 41.2% and AI adoption disclosures from 13.8% to 45.2%, with most risk mentions clustering around major vendors like Microsoft. Disclosure varies by sector and market segment, with Critical National Infrastructure and AIM reports lagging, and substantive risk disclosures remain rare—only 4.3% in 2025.
"Why It Matters": The findings show that while AI risk is increasingly referenced in corporate reports, substantive disclosures are scarce, highlighting a gap in transparency that could affect societal resilience.
By Bart Jaworski
arXiv:2606. 11217v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and autonomous AI agents has given rise to a rapidly growing methodological paradigm: "in silico" behavioral experiments.
By Michelle Vaccaro