A source-anchored examination of the 2020 U.S. Census that separates documented enumeration errors from differential-privacy effects and proposes a 2030 audit architecture. The work distinguishes between accuracy losses attributable to disclosure avoidance mechanisms and those caused by operational enumeration challenges, and offers a framework for evaluating representational integrity under privacy-constrained data publication.