MIT AI Risk Repository · Risk Sub-Category · 61.02.20
Detection challenges in content
Category: Sources of systemic risks from general-purpose AI
Description
"The difficulty in distinguishing synthetic content from authentic material adds to information risks."
From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Domain
- 3. Misinformation
- Causal entity
- Other
- Intent
- Other
- Timing
- Post-deployment
Subdomain definition: Highly personalized AI-generated misinformation creating “filter bubbles” where individuals only see what matches their existing beliefs, undermining shared reality, weakening social cohesion and political processes.
Real-world incidents in this subdomain
- Google Books Appears to Be Indexing Works Written by AI
- Uptick in Low-Quality AI-Produced Content Degraded Publishers' Submission Management
- Korean Politician Employed Deepfake as Campaign Representative
- Facebook Political Ad Delivery Algorithms Inferred Users' Political Alignment, Inhibiting Political Campaigns' Reach
How other frameworks describe this risk
- Information degradation
- Radicalisation
- Institutional trust loss
- Worsened epistemic processes for society
- Reduced decision-making capacity as a result of decreased trust in information
- Widespread use of persuasive tools contributes to splintered epistemic communities
- AI contributes to increased online polarisation
- Degradation of the information environment