Risks from models and algorithms (Risks of unreliable output)

Classification

Domain

3. Misinformation

Subdomain

3.1 False or misleading information

Causal entity

AI

Intent

Unintentional

Timing

Post-deployment

Subdomain definition: AI systems that inadvertently generate or spread incorrect or deceptive information, which can lead to inaccurate beliefs in users and undermine their autonomy. Humans that make decisions based on false beliefs can experience physical, emotional or material harms

Real-world incidents in this subdomain

• 12 May 2026Nonfiction Book 'The Future of Truth' Reportedly Included AI-Generated and Misattributed Quotations
• 6 May 2026Claude Console Reportedly Generated Phantom Legal Quotations in Trump Layoffs Court Filing
• 21 Apr 2026Purportedly AI-Enhanced Images of Iranian Women Protesters Were Reportedly Spread With Unverified Execution Claims
• 10 Apr 2026South Africa Draft National AI Policy Reportedly Included Fictitious References Believed to Be AI Hallucinations
• 8 Apr 2026Purportedly AI-Generated Image Reportedly Misled Daejeon Authorities Searching for Escaped Wolf Neukgu
• 17 Mar 2026Gemini and Grok Reportedly Misidentified Authentic Minab School-Strike Graveyard Photo as Unrelated Disaster Imagery

How other frameworks describe this risk

• Pursuing Consistent Context

  Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

• Knowledge Gaps

  Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

• Hallucinations

  Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

• Faithfulness Errors

  Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

• Defective Decoding Process

  Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

• Untruthful Content

  Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

• Factuality Errors

  Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

• Noisy Training Data

  Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

Other entries from TC2602024

• AI's inherent safety risks Risk Category
• Risks from models and algorithms (Risks of explainability) Risk Sub-Category · 7.4
• Risks from models and algorithms (Risks of bias and discrimination) Risk Sub-Category · 1.1
• Risks from models and algorithms (Risks of robustness) Risk Sub-Category · 7.3
• Risks from models and algorithms (Risks of stealing and tampering) Risk Sub-Category · 2.2
• Risks from models and algorithms (Risks of adversarial attack) Risk Sub-Category · 2.2
• Risks from data (Risks of illegal collection and use of data) Risk Sub-Category · 2.1
• Risks from data (Risks of improper content and poisoning in training data) Risk Sub-Category · 1.2
• Risks from data (Risks of unregulated training data annotation) Risk Sub-Category · 7.3
• Risks from data (Risks of data leakage) Risk Sub-Category · 2.1
• Risks from AI systems (Risks of exploitation through defects and backdoors) Risk Sub-Category · 2.2
• Risks from AI systems (Risks of computing infrastructure security) Risk Sub-Category · 2.2