AI-enabled Compliance Verification
Description
Federal agencies and recipients of federal funding must certify compliance with various civil rights statutes, but DOJ lacks efficient methods to verify the accuracy of these certifications. Manual review of compliance documentation is resource-intensive and often occurs only after complaints are filed, allowing violations to persist and potentially expand. False certifications can result in continued federal funding to non-compliant entities, undermining civil rights enforcement and wasting taxpayer resources. Aligns with E.O. 14179's innovation requirements and M-25-21's public trust and governance pillars.
Detailed example
Classifications, Predictions, Recommendations: compliance risk assessments, violation predictions, investigation recommendations.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
(1) Strengthens DOJ's ability to enforce civil rights laws through the False Claims Act by identifying clear-cut compliance violations earlier in the process. Protects taxpayer funds from flowing to entities that falsely certify compliance while ensuring federal programs achieve their intended civil rights objectives (2) Leverages FCA compliance databases, Civil Rights Division patterns, and initial whistleblower submission channels. (3) Focus on objective, verifiable metrics such as statistical disparities in outcomes, missing required documentation, or contradictions between certifications and published policies. Number of suspicious certifications flagged; investigations initiated; successful FCA settlements or recoveries.
Controls / human review
ATO: Not reported; PIA: Not published