urn:uuid:3e4eb938-a17b-5bfa-bf89-f9e025eb98d3 New versions of this debate, revisions of its claims, Radar updates and research opportunities. 2026-10-13T00:00:00Z SocioLens urn:uuid:476ae4d2-ec93-5697-ab09-59e08fae8ebc 2026-10-13T00:00:00Z

What changed: The public-facing claim now says that the advantage disappeared against coached elite debaters, the only comparison tested under the length and speed constraint, instead of implying it disappeared against all strong human persuaders. Why: A claim check on 13 October 2026 found that the HQ notes report the constrained result only against coached elite debaters (0.0pp, 95% CI −1.7 to +1.6), while version 1.0 attached it to the advantage over all strong human persuaders. The check and this revision were done by AI agents against the HQ notes, not by a person.

urn:uuid:3755d307-3753-56c0-a063-21fa1500a8a0 2026-10-13T00:00:00Z

What changed: The public-facing claim and scope no longer give a 48-market, about-100,000-respondent base for the 10% figure; they say "across surveyed markets" and add the 7% of a year earlier that HQ verified. Why: A claim check on 13 October 2026 found that HQ verified the 10% figure only as a global aggregate (up from 7%) and recorded 27 markets for a different question; the 48-market base for this figure was never verified, and one secondary report gives 45 markets for the chatbot questions. An unverified number is removed rather than guessed. The check and this revision were done by AI agents, not by a person.

urn:uuid:bc923090-8886-5f73-a419-9b5650eb3895 2026-10-13T00:00:00Z

What changed: The public-facing claim now says the study does not establish that regulation causes the gap, instead of saying it does not identify regulation as the "sole" cause. Why: A claim check on 13 October 2026 found that "not the sole cause" implies the study shows regulation to be a partial cause; the HQ primary read of Pan & Xu says the study explicitly does not establish a causal relationship between regulation and censorship behaviour. The check and this revision were done by AI agents against the HQ notes, not by a person.

urn:uuid:fa72f85a-1041-58e8-b506-994b1f91516b 2026-10-13T00:00:00Z

What changed: The claim now says that generative AI systems already on the market before 2 August 2026 have until 2 December 2026 to meet the Article 50(2) machine-readable marking obligation, and cites Regulation (EU) 2026/1744 (the Digital Omnibus on AI) as a third Source. The 2 August 2026 date for the other Article 50 obligations is unchanged. Why: A newer-evidence check on 13 October 2026 found that version 1.0 missed the Digital Omnibus on AI (Regulation (EU) 2026/1744 of 8 July 2026, published in the Official Journal on 24 July 2026, before the 25 September evidence cut-off), so a reader could take every Article 50 duty as binding from August. Recital 38 of the Regulation sets a four-month transitional period for providers who placed their systems on the market before 2 August 2026, which ends on 2 December 2026; that date is also stated by law-firm summaries (A&O Shearman; Ropes & Gray, 3 August 2026). The recital was read on EUR-Lex by an AI agent, not a person. The operative provision, Article 111(4) of Regulation (EU) 2024/1689 as inserted by Article 1, point (39)(b), of Regulation (EU) 2026/1744 (providers of AI systems generating synthetic content placed on the market before 2 August 2026 "shall take the necessary steps in order to comply with Article 50(2) by 2 December 2026"), was read by an AI agent in a secondary consolidated copy, and a second AI review run by the Owner located it in the EUR-Lex text.

urn:uuid:6f4dd434-e2d6-57e4-9f02-1afa239fa4e7 2026-10-13T00:00:00Z

Literature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.

urn:uuid:4ae31d95-8871-51a2-9c91-4971010b6e2f 2026-10-13T00:00:00Z

Literature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.

urn:uuid:4abb1996-dbce-5cac-ba50-511cb0daf1f5 2026-10-13T00:00:00Z

Narrowed after the literature check. The local-language and local-country sourcing part of the question is now partly answered: Zha and Chang audit Google and Baidu AI Overviews for health queries in 12 country settings and 4 languages and find that native-language queries raise locally sourced citations 3.5 to 13.5 times, Li et al. compare Baidu and Google overview source exposure across English and Chinese, and Zhen et al. map citations in Chinese generative search. None of these checks whether claims are supported by the cited pages (Zha and Chang name claim-level checking as future work), and the only grounding checks found (Xu et al.; Huang et al.; the Oumi analysis for the New York Times) are English and US-located. The opportunity should be narrowed to claim-level, human-validated fidelity across languages and markets, with source localization as a secondary measure.

urn:uuid:657182af-8ffe-5516-8b2d-6d564963ccd8 2026-10-13T00:00:00Z

Literature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.

urn:uuid:4c1fcfde-e59d-595e-a007-72c917c961bf 2026-10-13T00:00:00Z

Literature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.

urn:uuid:23a9d22a-ce09-5bcc-8b6e-d4aea8dc85c9 2026-10-13T00:00:00Z

Cost band corrected from "low to medium" to "medium": participant payments of about US$10,000 to 25,000 need a grant or project budget, in line with other opportunities of that size. Found in the independent review of the opportunity-page fixes. The question, design, literature check and verdict are unchanged.

urn:uuid:6f9370be-d27d-594d-a484-f5ce625d8f26 2026-10-13T00:00:00Z

Literature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The verdict changed from "No study found doing the core design" to "Related work exists" because related work turned up; the question stands.

urn:uuid:cb76ef24-6d0c-5a78-a000-bba08dccda0b 2026-10-12T00:00:00Z

When AI answer layers in search (Google AI Overviews and AI Mode) are shown or hidden at random on people's own smartphones, in at least one non-US market and language, how much do they change clicks to external websites, zero-click searches, the kinds of sources visited and trust in the information, compared with the US desktop estimates?

urn:uuid:7404907f-071a-5deb-b85c-29817cc09350 2026-10-12T00:00:00Z

Among people whose browsing is tracked across devices and platforms in 2025–26, does exposure to AI answers built into search (AI Overviews, AI Mode) and to standalone chatbots change the number, concentration and type of websites and news sources they visit over months, and does the change differ from what was found for early chatbot adoption?

urn:uuid:03550ede-4d46-5d76-84b6-7f2943062da7 2026-10-12T00:00:00Z

For matched public-interest queries (news, health, elections) issued in several languages and country markets, how often do claims in Google AI Overviews and AI Mode lack support in the pages they cite, how often do they cite sources in the user's own language and country, and do these rates differ from the US English baseline?

urn:uuid:b650700f-c704-5f9b-82de-088b908a9669 2026-10-12T00:00:00Z

When people are randomly assigned to search with AI answers as the default, or without them, for eight to twelve weeks of their normal information seeking, how do their source-checking behaviour, trust in AI answers, ability to spot wrong answers and reliance on AI for news and health questions change over time, and do early effects fade, persist or grow?

urn:uuid:977e0e48-b822-53f6-a34e-e69e07152284 2026-10-12T00:00:00Z

On a live, open social platform, when labels saying that a post is AI-generated, or that it is AI-generated and potentially misleading, are applied at random to real political and news posts in users' feeds, how do they change actual reposts, likes and replies, and users' belief in the labelled claims, compared with unlabelled posts?

urn:uuid:765359c7-60b1-52ae-b10b-8eb09ebfda01 2026-10-12T00:00:00Z

Pooling randomized experiments published from 2023 to 2026, including those testing 2025–26 frontier models, how large is the persuasive effect of AI messages and conversations, relative to no message and to human persuaders, and how does it vary with model generation, conversation format, message length, topic, country and outcome type (attitude, vote intention, behaviour, persistence at follow-up)?

urn:uuid:a4513e23-3db3-5c99-af8e-20ce69622ea7 2026-09-25T00:00:00Z

When, and through which mechanisms, does generative AI change persuasion, information intermediation and the public information environment?