AI Alignment
Also known as: Value Alignment, AI Safety Alignment
The research field focused on ensuring that AI systems' goals, behaviors, and values are compatible with human intentions and societal well-being throughout their operation.
“The research field focused on ensuring that AI systems' goals, behaviors, and values are compatible with human intentions and societal well-being throughout their operation.
“
Overview
AI alignment is the challenge of ensuring that AI systems do what their developers and users actually want them to do, in the way they want them to do it. As AI systems become more capable, the difficulty of specifying and maintaining alignment with human values grows correspondingly. Misaligned AI — systems that pursue objectives different from their creators' intentions — could range from annoying to dangerous.
The Alignment Problem
The core challenge is that it's extremely difficult to precisely specify what we want an AI to do, along with all the implicit constraints, edge cases, and value judgments that humans take for granted. A system optimizing for a poorly specified objective can find unexpected and undesirable ways to achieve that objective.
Alignment Techniques
RLHF (Reinforcement Learning from Human Feedback)
Training models using human evaluations of output quality to learn human preferences. This is the primary technique used by OpenAI, Anthropic, and Google to align their language models.
Constitutional AI
Developed by Anthropic, this approach defines a set of principles (a "constitution") that guides the model's behavior, reducing reliance on human feedback for every decision.
Red-Teaming
Systematically testing AI systems by attempting to elicit harmful, biased, or misaligned outputs, then using the findings to improve the system.
Context Management and Alignment
Context management plays a critical role in alignment. The context provided to an AI system — including system prompts, retrieved documents, and conversation history — shapes its behavior. Well-managed context helps keep AI systems aligned with their intended purpose, while poor context management can inadvertently cause misalignment.
Sources & Further Reading
Related Terms
Artificial General Intelligence
A hypothetical form of AI that possesses the ability to understand, learn, and apply intelligence across any intellectual task that a human being can, exhibiting flexibility and adaptability across domains.
Hallucination
When an AI model generates information that sounds plausible but is factually incorrect, fabricated, or not supported by its training data or provided context.
Reinforcement Learning from Human Feedback
A training technique that uses human evaluations of AI outputs to train a reward model, which then guides the AI system to produce outputs more aligned with human preferences.
Responsible AI
The practice of designing, developing, deploying, and using AI systems in ways that are ethical, transparent, fair, accountable, and aligned with human rights and societal values.