AI Alignment: Types and Risks

• Alignment, in the context of AI, seeks to create AI systems that are consistent with the values of both their users and broader society.
• It's important due to the increasing power of AI and the potential for misalignment to cause significant harm.
• Achieving alignment involves formal methods, technical AI alignment, iterative AI alignment, value elicitation, and governance structures.

Types of Alignment

• Intentional Alignment refers to AI systems behaving as their designers intended, with a well-defined objective function.
• Societal Alignment aims for AI behavior which benefits society, considering ethical implications, reducing biases, and promoting fairness.
• Human Alignment involves AI aligning with individual human values and preferences, being customizable to personal ethics.

Risks of Misalignment

• Specification Gaming refers to AI optimizing for a specified objective in unintended or harmful ways.
• Reward Hacking is where AI exploits loopholes to maximize rewards without achieving the intended outcome.
• Power Seeking is where AI aims to gain resources and control, possibly at the expense of human autonomy.
• Value Drift refers to AI's values changing over time, misaligning with initial goals.

Formal Methods

• Formal methods use mathematical techniques for specifying, developing, and verifying software and hardware systems.
• They can verify AI alignment by specifying values in a formal language.
• AI systems developed must satisfy those specifications and existing systems can be verified against them.
• Benefits of formal methods include increased safety, early error detection, and better communication between developers and stakeholders.

Technical AI Alignment

• Technical AI alignment involves developing AI systems that are aligned with our values by design.
• This includes value learning, inverse reinforcement learning, preference learning, and Explainable AI (XAI).

Iterative AI Alignment

• Iterative AI alignment is the process of training AI systems to align with our values through trial and error.
• Achieved through reinforcement learning from human feedback (RLHF), active learning, and adversarial training.

Eliciting Values

• Value elicitation is the process of identifying and formalizing human values.
• Important because we must understand our values before aligning AI systems.
• Achieved through surveys, interviews, experiments, and ethnography.

Governance

• AI Governance involves creating policies and regulations to ensure AI is developed and used responsibly.
• It focuses on safety, transparency, and accountability, addressing ethical concerns and societal impacts.
• Goals include establishing safety standards, ethical guidelines, promoting transparency, international cooperation, bias detection, and accountability.
• Key stakeholders include governments, industry, academia, and civil society.