MIT AI Risk Repository · Risk Sub-Category · 53.01.08
Harms from increasingly agentic algorithmic systems
Category: Alignment failures in existing ML systems
Description
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From Advancing AI Governance: A Literature Review of Problems, Options, and Proposals (Maas2023), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
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
Other entries from Maas2023
- Alignment failures in existing ML systems
- Faulty reward functions in the wild
- Specification gaming
- Reward model overoptimization
- Instrumental convergence
- Goal misgeneralization
- Inner misalignment
- Language model misalignment
- Dangerous capabilities in AI systems
- Situational awareness
- Acquisition of a goal to harm society
- Acquisition of goals to seek power and control