AI-enabled Compliance Verification

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

High-impactSource: a) High-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Law Enforcement

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

(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.

System outputs

Classifications, Predictions, Recommendations: compliance risk assessments, violation predictions, investigation recommendations.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported