Data Quality Documentation

Data quality documentation is a record of how an organization collects, processes, and validates the data used to train and operate AI systems. This documentation helps compliance teams demonstrate that AI outputs are based on reliable inputs, which is essential for meeting regulatory requirements around transparency and accountability. Without clear documentation of data sources, cleaning processes, and quality checks, organizations struggle to explain decisions made by AI systems to regulators, customers, or affected individuals in legal disputes.

1 item

EnforcementUS2026-09-29

IRS Deployed High-Impact AI With No Testing Records in 80% of Cases

The Treasury Inspector General for Tax Administration (TIGTA) found that four of five high-impact IRS AI use cases had no testing documentation. This was true even though data quality checks were being performed. TIGTA concluded this creates undetectable risk of inaccurate, biased, or unreliable AI outputs. IRS management agreed to standardize procedures and complete AI impact assessments for deployed systems by November 2026.

IRS federal AI compliance AI use case inventory data quality documentation OMB requirements TIGTA AI impact assessment audit readiness