MIT AI Risk Repository · Risk Sub-Category · 70.02.02

Misinformation

Category: Informational Risks

Open the MIT Risk Navigator All entries from Perlo2025

Description

"Non-embodied AIs are known to propagate misinformation [81, 82]. Various studies have shown that LLMs hallucinate information, including academic citations [83], clinical knowledge [84], and cultural references [85]. EAI systems inherit these shortcomings in the physical world, answering user questions with deceptive or incorrect information [86]. Because VLAs fuse vision and language, their hallucinatory failures can be spatially grounded—e.g., misidentifying an object in view and then generating a plausible yet unsafe action plan around it. And although automated home assistants like Amazon

From Embodied AI: Emerging Risks and Opportunities for Policy Action (Perlo2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).

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

Browse all incidents in this subdomain

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 Perlo2025

Physical Risks Risk Category
Purposeful or malicious harm Risk Sub-Category · 4.2
Purposeful or malicious harm Additional evidence
Accidental harm Risk Sub-Category · 7.3
Accidental harm Additional evidence
Informational Risks Risk Category
Privacy Violations Risk Sub-Category · 2.1
Privacy Violations Additional evidence
Misinformation Additional evidence
Economic Risks Risk Category · 6.0
Labour Displacement Risk Sub-Category · 6.2
Labour Displacement Additional evidence