MIT AI Risk Repository · Risk Sub-Category · 45.02.13
Ethical Risks (Risks of AI becoming uncontrollable in the future)
Category: Safety risks in AI Applications
Description
"With the fast development of AI technologies, there is a risk of AI autonomously acquiring external resources, conducting self-replication, become self-aware, seeking for external power, and attempting to seize control from humans."
From AI Safety Governance Framework (TC2602024), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- AI
- Intent
- Intentional
- Timing
- Post-deployment
Subdomain definition: AI systems acting in conflict with human goals or values, especially the goals of designers or users, or ethical standards. These misaligned behaviors may be introduced by humans during design and development, such as through reward hacking and goal misgeneralisation, or may result from AI using dangerous capabilities such as manipulation, deception, situational awareness to seek power, self-proliferate, or achieve other goals.
Real-world incidents in this subdomain
- Reinforcement Learning Reward Functions in Video Games
- Predictive Policing Program by Florida Sheriff’s Office Allegedly Violated Residents’ Rights and Targeted Children of Vulnerable Groups
- Image Classification of Battle Tanks
How other frameworks describe this risk
- Natural Language Underspecifies Goals
- Loss of control
- Loss of control
- Sudden loss of control
- AI leads to humans losing control of the future
- Risks from delegating decision-making power to misaligned AIs
- Risks from AIs developing goals and values that are different from humans
- Future AI systems might actively reduce human control
Other entries from TC2602024
- AI's inherent safety risks
- Risks from models and algorithms (Risks of explainability)
- Risks from models and algorithms (Risks of bias and discrimination)
- Risks from models and algorithms (Risks of robustness)
- Risks from models and algorithms (Risks of stealing and tampering)
- Risks from models and algorithms (Risks of unreliable output)
- Risks from models and algorithms (Risks of adversarial attack)
- Risks from data (Risks of illegal collection and use of data)
- Risks from data (Risks of improper content and poisoning in training data)
- Risks from data (Risks of unregulated training data annotation)
- Risks from data (Risks of data leakage)
- Risks from AI systems (Risks of exploitation through defects and backdoors)