Information Integrity and Public Trust
The Global Risks Forum (GRF)
Generative AI produces text, images, audio and video that can be hard to tell from human work. It lowers the cost of producing persuasive and false content, and of impersonating real people. Governance responds with provenance standards, labelling duties and platform rules, each with limits. This page sets out how AI-generated information affects integrity, the records that show it, and who decides.
AI generated information · Crisis information cell · Week 34
| Tap water poisoned after flood | 81 | High | False | Correction out |
| Shelters charging fees | 64 | Medium | False | Drafting |
| Dam about to fail | 69 | High | Checking | Holding line |
| Aid only for registered voters | 22 | Medium | False | Prebunked |
Fig.Narrative monitor. Design wireframe with placeholder names and illustrative values, not a record or a live system.
AI-generated content is text, images, audio or video produced by generative AI, which can be hard to tell from human work. It lowers the cost of persuasion, deception and impersonation. Provenance standards, labelling duties and platform rules respond, with limits. Legislators, platforms, electoral authorities and newsrooms hold the decisions.
For ai generated information, in GRF's situational-awareness reading, the map opens on population density and night lights with night lights tonight from NASA GIBS, framed on Eastern Europe; 4 further NASA layers wait in the list.
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NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
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For ai generated information, in GRF's situational-awareness reading, the map opens on population density and night lights with night lights tonight from NASA GIBS, framed on Eastern Europe; 4 further NASA layers wait in the list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Layers and dates
Synthetic media and authenticity. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Generating plausible text, images and voices is cheap and fast. Volume overwhelms review, and low-quality synthetic content fills search results and feeds.
Voice and video clones of real people enable fraud, harassment and political deception. Victims include public figures and private individuals.
Detectors produce false positives and miss content that has been edited or compressed. Reliance on detection alone can wrongly accuse genuine content.
When anything could be fake, genuine evidence can be dismissed as synthetic. This liar's dividend weakens documentation of real events.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Deepfake | Synthetic or manipulated media that realistically depicts a person saying or doing something they did not. |
| Content credentials | Provenance information attached to media under the C2PA standard, recording creation and edits. |
| Liar's dividend | The benefit to those who dismiss genuine evidence as fake when synthetic media are known to exist. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Platforms implementing content credentials | count | on change | C2PA and platform records | DECLARED |
| Synthetic content labelling rules in force | count of jurisdictions | on change | Legislation | OFFICIAL |
| Reported deepfake incidents | count by harm type | monthly | Incident databases | REPORTED |
| Detection tool accuracy | true and false positive rates | per evaluation | Independent benchmarks | ESTIMATED |
| Election-related synthetic media incidents | count | per election | Monitoring reports | REPORTED |
| Public ability to identify synthetic media | share correct in tests | per study | Experimental studies | ESTIMATED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| C2PA specifications and conformance records | Provenance standard and adopters | Free, C2PA terms |
| OECD AI Incidents Monitor | Incidents including synthetic media harms | Free, OECD terms |
| AI Incident Database | Reported incidents involving AI | Open, licence to check |
| Partnership on AI Responsible Practices for Synthetic Media | Framework and case studies for synthetic media | Free to read, terms to check |
| Electoral authority and observer reports | Synthetic media in election campaigns | Public records, terms per publisher |
Information Integrity and Public Trust prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Legislatures and AI regulators | What labelling and disclosure duties apply to synthetic content | Comparative rules and evidence on effects |
| Platforms and AI developers | Whether to attach provenance and how to label content | Standards and implementation practice |
| Electoral authorities | How to address synthetic media in campaigns | Monitoring reports and comparable rules |
| Newsrooms | How to verify media and disclose AI use | Verification practice and standards |
Researchers on AI-generated information ask how to keep authentic evidence trustworthy.
Generative AI lowers the cost of producing convincing false content, including impersonation of public figures and fabricated evidence. It also enables personalised and multilingual content at scale. At the same time, the mere possibility of synthetic media allows genuine evidence to be dismissed as fake, a dynamic sometimes called the liar's dividend.
Detection tools can identify some synthetic content, but they are imperfect and engage in a continuing race with generation methods. Provenance approaches, which attach verifiable information about origin and editing to content, offer a complementary path, but depend on wide adoption and can be stripped. No single technical measure solves the problem; institutional practices and media literacy matter alongside technology.
Developing and evaluating classifiers with artificial intelligence and machine learning. Accuracy degrades as generators improve.
Evaluating content credentials and watermarking. Adoption is incomplete and marks can be removed.
Studying how people judge authenticity with social and behavioural science. People overestimate their ability to spot fakes.
Studying real cases of synthetic media harm. Cases are often reported selectively.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.