The four pillars of AI alignment, known as the RICE principles.
Robustness: Refers to the AI system's ability to operate reliably in various environments and resist unexpected interference.
Interpretability: Requires us to understand the internal reasoning process of the AI system, especially opaque neural networks. Through interpretability tools, the decision-making process is made open and understandable to users and stakeholders, thereby ensuring the system's safety and operability.
Controllability: Ensures that the behavior and decision-making process of the AI system are subject to human supervision and intervention. This means that humans can correct deviations in system behavior in a timely manner, ensuring that the system remains aligned during deployment.
Ethicality: AI systems adhere to socially recognized ethical standards in decision-making and actions, respecting the values of human society.
The four pillars of AI alignment, known as the RICE principles.
Robustness: Refers to the AI system's ability to operate reliably in various environments and resist unexpected interference.
Interpretability: Requires us to understand the internal reasoning process of the AI system, especially opaque neural networks. Through interpretability tools, the decision-making process is made open and understandable to users and stakeholders, thereby ensuring the system's safety and operability.
Controllability: Ensures that the behavior and decision-making process of the AI system are subject to human supervision and intervention. This means that humans can correct deviations in system behavior in a timely manner, ensuring that the system remains aligned during deployment.
Ethicality: AI systems adhere to socially recognized ethical standards in decision-making and actions, respecting the values of human society.