MIT AI Risk Repository · Risk Sub-Category · 62.23.03
Deceptive behavior because of an incorrect world model
Category: Agency (Deception)
Description
"AI systems can create deceptive outputs because their learned world model is not an accurate model of the real world [210]."
From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
Subdomain definition: AI systems that develop, access, or are provided with capabilities that increase their potential to cause mass harm through deception, weapons development and acquisition, persuasion and manipulation, political strategy, cyber-offense, AI development, situational awareness, and self-proliferation. These capabilities may cause mass harm due to malicious human actors, misaligned AI systems, or failure in the AI system.
How other frameworks describe this risk
- Safety Risks from Affordances Provided to LLM-agents
- Agentic LLMs Pose Novel Risks
- Goal-Directedness Incentivizes Undesirable Behaviors
- Capabilities that could be used to reduce human control - Cyber offence
- Capabilities that could be used to reduce human control - Autonomous replication and adaptation
- Capabilities that could be used to reduce human control - Manipulation
- Subagents
- AI Influence