MIT AI Risk Repository · Additional evidence · 73.05.00a
Socioeconomic Impacts of LLM May Be Highly Disruptive
Description
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From Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Domain
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- Subdomain
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- Causal entity
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- Intent
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- Timing
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Other entries from Anwar2024
- Agentic LLMs Pose Novel Risks
- Natural Language Underspecifies Goals
- Goal-Directedness Incentivizes Undesirable Behaviors
- Safety Risks from Affordances Provided to LLM-agents
- Multi-Agent Safety Is Not Assured by Single-Agent Safety
- Foundationality May Cause Correlated Failures
- Groups of LLM-Agents May Show Emergent Functionality
- Collusion between LLM-Agents
- Dual-Use Capabilities Enable Malicious Use and Misuse of LLMs
- Misinformation and Manipulation
- Cybersecurity
- Cybersecurity