Value Alignment
Noun · AI & Machine Learning
Definitions
Value Alignment is a safety or governance control for limiting harmful, noncompliant, or insecure model behavior. It is commonly used for production AI systems that need policy enforcement and auditability, where teams need predictable behavior under real workloads rather than toy examples. Practitioners pay attention to coverage, false positives, and adversarial pressure, because those factors usually determine whether the approach improves quality, latency, reliability, or operating cost in production.
In plain English: Value Alignment is an AI concept teams use to train models, guide predictions, or make model behavior more reliable and easier to control in practice.
Example: "We evaluated Value Alignment in the new model pipeline because the baseline was plateauing; once it was wired into training and evaluation, quality improved enough to justify rolling it into the next release."