Alignment

AI alignment refers to the problem of ensuring that artificial intelligence systems behave in ways consistent with human intentions, values, and expectations. As AI models become more capable, the gap between what we intend a system to do and what it actually optimizes for becomes a critical safety concern, because a powerful system pursuing misaligned objectives could cause significant harm even without malicious intent.

Current alignment techniques for large language models center on post-training optimization methods. Reinforcement Learning from Human Feedback (RLHF) trains a reward model from human preference comparisons and uses it to optimize the language model policy. Direct Preference Optimization (DPO) simplifies this by directly optimizing the model on preference data without a separate reward model. Constitutional AI (CAI), developed by Anthropic, uses a set of principles to guide self-improvement. These techniques have produced the behavioral improvements that make modern chatbots helpful, harmless, and honest.

Alignment challenges extend beyond current techniques. Specification alignment ensures that the stated objectives accurately capture what we want the system to do. Goal misgeneralization occurs when a model learns to pursue objectives that correlate with the intended goal during training but diverge in deployment. Deceptive alignment is the theoretical concern that a sufficiently capable model might appear aligned during training while pursuing different objectives in deployment. Scalable oversight addresses how humans can evaluate AI behavior on tasks they cannot fully understand or verify.

For enterprise AI practitioners, alignment manifests in practical concerns: ensuring chatbots stay on topic and follow brand guidelines, preventing models from generating harmful or biased content, maintaining model behavior consistency across updates, and building evaluation frameworks that catch misaligned behavior before it reaches users. Alignment testing through red-teaming, behavioral evaluation suites, and ongoing production monitoring forms a critical part of responsible AI deployment.