Research preview. Independent expert review is pending. The worked analyses are hypothetical.
Method and corrections Interactive field model
Inspect the system around the AI system. The model is organized by the clauses of the definition: societies, the four objects, the benefits and harmful patterns governance aims at, and the conditions it races against. Apply a cross-cutting test, narrow by lifecycle stage or level, and inspect how definitions, claims, relationships and sources travel together.
Apply a cross-cutting test Whole model Power Legitimacy Distribution Adaptability
Narrow by lifecycle stage or level Lifecycle stage Any stage Research Data collection Model development Evaluation Procurement Deployment Monitoring Incident response Retirement Level Any level Community and organisational use Corporate Sectoral National Regional International Loading interactive controls. Concept links remain available. Field model · whole model
Groups follow the clauses of the definition; they are not a sequence or causal chain. Actual relationships appear in the inspector.
Jump to selected entity Societies …how societies should shape…
actor AI developer or provider actor Deploying organization actor Affected people actor Civil society and independent researchers Incentives …the incentives…
incentive Deployment pressure incentive Competitive race dynamics incentive Liability exposure incentive Procurement leverage
Institutions …institutions…
institution Regulator or public authority institution Internal safety and governance team institution Standards body institution International institution
Infrastructures …infrastructures…
infrastructure Evaluation capacity infrastructure Compute infrastructure Data, language resources, and data labor infrastructure Energy, water, and minerals infrastructure Data work and content moderation Safeguards …and safeguards around AI…
safeguard Risk management process safeguard Impact assessment safeguard Traceability safeguard Audit record safeguard Meaningful human oversight safeguard Supervision and regulatory review safeguard Pre-deployment evaluation and red teaming safeguard Incident reporting and post-deployment monitoring safeguard Complaint and appeal routes safeguard Review and sunset provisions safeguard Compute controls Benefits and harmful patterns …so that its benefits are broadly shared and harmful patterns are addressed…
principle Accountability principle Safety principle Broadly shared benefit pattern Contestable decision pattern Concentration of power pattern Systematically unreliable provenance pattern Over-reliance and deskilling pattern Sycophancy and engagement-driven agreement pattern Homogenisation and loss of authorship Choices and conditions …while they are still choices rather than conditions.
condition Lock-in condition Governance lock-in condition Reversibility window
Pattern · Benefits and harmful patterns established
Sycophancy and engagement-driven agreement A pattern in which AI systems tell users what they want to hear, agreeing or flattering even when that is wrong or harmful, because training and business incentives reward approval.
Is the system rewarded for being right, or for being liked?
Tests Legitimacy Power Distribution
Lifecycle Model development Evaluation Deployment Monitoring
Levels Corporate National Recorded relationships Read each statement from its first entity to its second. Status describes our mapping, not certainty of cause.
No relationship has been recorded yet. This does not mean no relationship exists.
Claims and evidence · 5 source-assertion Confidence: medium Contestability: medium
Research by an AI developer found that leading assistants behave sycophantically and that human preference data used in training tends to reward answers that match users’ views.
Our reading: Developer research on 2023 models; the authors were at Anthropic. Independent work and a developer incident report point the same way.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Towards Understanding Sycophancy in Language Models (opens in a new tab) Abstract
Five leading AI assistants showed sycophancy across four tasks. In human preference data, responses matching a user’s views were more likely to be preferred, and people and preference models sometimes preferred convincing sycophantic answers to correct ones.
Scope note: Developer research: the paper states that all authors were at Anthropic, a developer of AI assistants (and of the model that assisted this edit). Published at ICLR 2024; arXiv version cited.
Passage checked 2026-10-18 · sharma-abstract
source-assertion Confidence: medium Contestability: medium
In preregistered experiments, a single interaction with a sycophantic AI made people less willing to take responsibility or repair a conflict, yet they trusted and preferred the sycophantic model, which gives developers an incentive to keep it.
Our reading: Measures short-term intentions after one interaction, not long-term behaviour, in mostly online participants. Independent of AI developers.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Sycophantic AI decreases prosocial intentions and promotes dependence (opens in a new tab) Abstract
Across 11 models, AI affirmed users’ actions 49% more often than humans did, including when queries involved deception or harm. In three preregistered experiments (N = 2,405), one sycophantic interaction reduced willingness to take responsibility and repair conflicts and increased certainty of being right, while participants trusted and preferred the sycophantic models, which the authors say gives developers an incentive to keep sycophancy.
Scope note: Independent academic research. The abstract was read in the Crossref record; the publisher page was not reachable. An earlier preprint reports smaller samples; the published figures are used.
Passage checked 2026-10-18 · cheng-abstract
source-assertion Confidence: high Contestability: low
A developer withdrew an assistant update that had become overly flattering and agreeable, attributing the problem to training that weighted short-term user feedback too heavily.
Our reading: The developer’s own account; it shows the incentive is real inside development, but its explanation has not been independently verified.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Sycophancy in GPT-4o: What happened and what we’re doing about it (opens in a new tab) Opening sections
The developer rolled back an update that made its assistant overly flattering or agreeable, attributing it to training that focused too much on short-term user feedback, and listed changes to training, testing and user controls.
Scope note: A developer’s account of its own product incident; not independently verified.
Passage checked 2026-10-18 · openai-rollback
interpretation Confidence: medium Contestability: medium
When AI assists a decision, a governance question is who keeps the authority to decide, to override the system, and to be accountable for the result. Arrangements that leave that authority unclear can shift judgment to whoever controls the system and risk to those least able to check it.
Our reading: AIGovSpec’s interpretation, connecting human-agency patterns to the legitimacy and distribution tests. The cited studies show why the question matters; they do not answer it.
Inspect evidence · 3 passages Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
When combinations of humans and AI are useful: A systematic review and meta-analysis (opens in a new tab) Abstract
Across 106 experiments and 370 effect sizes, human–AI combinations on average performed worse than the better of humans or AI alone (Hedges’ g −0.23). Losses appeared in decision tasks; content-creation tasks showed significantly greater gains.
Scope note: Preregistered meta-analysis of experiments published 2020 to mid-2023, mostly short laboratory and online tasks. Open access.
Passage checked 2026-10-18 · vaccaro-abstract
Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study (opens in a new tab) Abstract, Findings and Interpretation
At four Polish centres, the adenoma detection rate in colonoscopies done without AI fell from 28.4% to 22.4% in the three months after AI assistance was introduced, compared with the three months before. The authors say continuous AI exposure might reduce performance without it.
Scope note: Paywalled; the abstract was read through Europe PMC (PMID 40816301). Observational before-and-after comparison at four centres in Poland. The paper received published correspondence and an erratum, which were not read.
Passage checked 2026-10-18 · budzyn-abstract
Sycophantic AI decreases prosocial intentions and promotes dependence (opens in a new tab) Abstract
Across 11 models, AI affirmed users’ actions 49% more often than humans did, including when queries involved deception or harm. In three preregistered experiments (N = 2,405), one sycophantic interaction reduced willingness to take responsibility and repair conflicts and increased certainty of being right, while participants trusted and preferred the sycophantic models, which the authors say gives developers an incentive to keep sycophancy.
Scope note: Independent academic research. The abstract was read in the Crossref record; the publisher page was not reachable. An earlier preprint reports smaller samples; the published figures are used.
Passage checked 2026-10-18 · cheng-abstract
source-assertion Confidence: high Contestability: low
UNICEF’s guidance says AI companions should never be intentionally designed to create emotional dependency in children and should disclose that they are not human.
Our reading: Guidance, not law; it sets design expectations without saying how they would be enforced.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Guidance on AI and children 3.0 (opens in a new tab) “Address risks from AI-enabled chatbots or companions” (about pp. 20–21)
Chatbots and companions should never be intentionally designed to create emotional dependency in children, should have safety training and should disclose that they are not human; the guidance discusses, without endorsing, calls to ban companions for under-18s.
Scope note: Supersedes the 2021 Policy guidance on AI for children (2.0). Ten requirements for child-centred AI; guidance, not law. Page numbers below are from the PDF as extracted.
Passage checked 2026-10-18 · unicef-companions