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Snapshot · OMB 2025 inventory · processed Oct 11, 2026
DHS · DHS-14 · record R1947

Biometrics Enrollment Tool (BET) Fingerprint Quality Check

Department of Homeland Security · USCIS

Overview

Development stage
DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation
Presumed high-impact, determined notSource: b) Presumed high-impact, but determined not high impact
Impact justification
The AI outputs do not serve as a principal basis for a decision or action concerning a specific individual or entity that has a legal, material, binding, or similarly significant effect on that individual’s or entity’s civil rights, civil liberties or privacy, equal opportunities, or access to or the ability to apply for critical government resources or services. This tool simply identifies whether a fingerprint collected is of sufficient quality to pass the FBI fingerprint check process, ultimately maximizing the number of successful FBI submissions while minimizing the number of fingerprint recaptures necessary. This quality assurance step is one task in a series of adjudication activities but is not determinative of the overall adjudication decision. The tool saves personnel time and resources while enhancing customer experience by helping to ensure that only quality fingerprints are passed forward for matching against the FBI Identity History Summary Check. Results of FBI fingerprint checks are subsequently reviewed by a human as part of the immigration adjudicative process.
Start date
2024-01-01 (day precision)Source: 2024-01-01T00:00:00
Withheld from public reporting
Not reported

Mission

Topic area
Law Enforcement
Operational functions
Biometrics and identity Derived by keyword rules; see methodology

Problem the AI is intended to solve

This effort aims to maximize the number of successful FBI submissions while minimizing the number of fingerprint recaptures necessary. The output is a Numerical Fingerprint Quality score, which is compared against fingerprint quality thresholds (per finger and per set of fingerprints) to align with FBI specifications.

Expected benefits

BET assists in determining if the fingerprint taken is good enough quality to pass the FBI fingerprint check process. It provides immediate feedback when a set of prints is likely to be rejected by the FBI by incorporating machine learning models into the BET application. The FBI will not disclose their quality grading criteria for fingerprints, leaving BET with the responsibility of determining quality to prevent unnecessary secondary encounters with applicants.

System outputs

Numerical Fingerprint Quality score, which is compared against fingerprint quality thresholds (per finger and per set of fingerprints) to align with FBI specifications

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)
Biometrics Enrollment Tool
Custom-developed code
Yes
Public source code
Not reported

Sourcing

How it was built
Contract and in-houseSource: c) Developed with both contracting and in-house resources
Vendor (as reported)
Pluribus Digital
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
Yes
Privacy Impact Assessment
www.dhs.gov ↗
Authorization to Operate
Yes
Demographic features
Not reported
Training and evaluation data
Internal data from BET data capture into Databricks lakehouse, numerical values representing fingerprint quality scores determined by the BET system outside of the AI workflow.
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.

Pre-deployment testingNo answer reported
AI impact assessmentNo answer reported
Independent reviewNo answer reported
Ongoing monitoringNo answer reported
Operator trainingNo answer reported
Fail-safeNo answer reported
Appeal processNo answer reported
User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported