Institutional trust loss

AI Risk

"Erosion of trust in public institutions and weakened checks and balances due to mis/disinformation, influence operations, or real or perceived misuse of generative AI"

Overview

A source-backed snapshot of this AI risk.

Techniques1Attack methods connected to this risk.

Mitigations0Defenses that may help with related attacks.

Records2Source records unified into this concept.

Risk profile

How the MIT AI Risk Repository categorizes this risk.

Domain4. Malicious Actors & Misuse; 3. Misinformation

Subdomain4.1 > Disinformation, surveillance, and influence at scale; 3.2 > Pollution of information ecosystem and loss of consensus reality

Entity1 - Human; 3 - Other

Intent3 - Other

Timing2 - Post-deployment

CategoryPolitical and Economic

SubcategoryInstitutional trust loss

Merged risk records

Source records unified into this canonical risk concept.

2 recordsView all →

MITRISK-Li2025-66.02.02 - Institutional trust loss

"Erosion of trust in public institutions and weakened checks and balances due to mis/disinformation, influence operations, or real or perceived misuse of generative AI"

Domain4. Malicious Actors & MisuseSubdomain4.1 > Disinformation, surveillance, and influence at scaleSourceA Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World IncidentsYear2025

MITRISK-Abercrombie2024-58.08.05 - Institutional trust loss

"Institutional trust loss - Erosion of trust in public institutions and weakened checks and balances due to mis/disinformation, influence operations, over-dependence on technology, etc."

Domain3. MisinformationSubdomain3.2 > Pollution of information ecosystem and loss of consensus realitySourceA Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation HarmsYear2024

Mitigations

Defenses that may help with related attacks.

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No propagated mitigations. No defense is available through the connected attack methods.

Source evidence

Research source for this risk, when available.

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Included resource

A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents

AuthorsLi et al.Year2025TypeConference Paper

DOI10.48550/arXiv.2505.22073 URLhttps://doi.org/10.48550/arXiv.2505.22073

Original source

MIT AI Risk Repository

Open the public repository used for AI risk records and taxonomy fields.

Repositoryhttps://airisk.mit.edu/