TLDR:
AI alignment is the field of research and engineering dedicated to ensuring that AI systems pursue the goals their designers and users actually want, rather than misinterpreting instructions, exploiting reward functions, or producing harmful outputs. Alignment is both a technical and a governance challenge, increasingly central to frontier AI development.
Alignment Techniques
Modern alignment combines multiple techniques: RLHF (training models on human preferences), Constitutional AI (using explicit principles to guide model behavior), Direct Preference Optimization (DPO, a simpler RLHF alternative), red-teaming (adversarial testing to find failure modes), interpretability research (understanding what model internals represent), and evaluations frameworks (benchmark suites measuring helpfulness, harmlessness, and honesty).
The Alignment Problem
Alignment is hard because human intent is rarely fully specifiable in advance—what we want depends on context, values, and outcomes that may not be visible at training time. Specific challenges include: reward hacking (the model gaming its training signal), specification gaming (technically meeting requirements while violating spirit), deceptive alignment (appearing aligned during training but behaving differently in deployment), and scaling oversight (humans cannot review all outputs of fast, prolific AI systems). These challenges intensify as models become more capable.
Industry and Regulatory Context
Anthropic, OpenAI, Google DeepMind, and other frontier labs have published voluntary safety commitments including alignment research investments, red-teaming, and disclosure of safety evaluations. The EU AI Act creates specific obligations for “systemic risk” general-purpose AI models including incident reporting and model evaluation. The UK AI Safety Institute, US AI Safety Institute, and similar bodies conduct independent evaluations of frontier models. For startups, alignment is most directly relevant when deploying powerful AI in high-stakes decisions—any system that affects user finances, health, or legal outcomes should have explicit alignment checks.