SSA · no agency ID · record R3103

Proactive Triage and Analysis of Hearings (PATH)

Social Security Administration · Chief Information Officer, Analytics and Improvements

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: High-impactSource: a) High-impact
Impact justification: Not reported
Start date: 2024-11-01 (day precision)Source: 11/1/2024 0:00
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Government Benefits Processing
Operational functions: Risk scoring and triage Derived by keyword rules; see methodology

Problem the AI is intended to solve

This AI use case flags high likelihood favorable claims and refers them to human adjudicators for further review to determine eligibility for on-the-record hearing decisions.

Expected benefits

The use case improves case flow, speeds up eligibility decision, increases consistency and accuracy, and provides better service to the public.

System outputs

The system outputs sorted case listings based on likelihood of favorable hearing decision.

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): PATH
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
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: Yes
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: c) Age; e) Socioeconomic Status
Training and evaluation data: Model used data from disability program data sets
Federal Data Catalog: Not reported

Governance

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

Pre-deployment testingReported in placeSource: a) Yes

AI impact assessmentReported in placeSource: a) Yes

Independent reviewReported in placeSource: c) Yes – by the CAIO

Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established

Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established

Fail-safeReported in placeSource: a) Yes

Appeal processNot applicableSource: b) Not applicable

User and public consultationOther or ambiguous answerSource: d) Other

Potential impacts and how they were identified

The positive impacts (faster eligibility decisions, reduce backlog, more efficient), and the negative impacts (risk of model misclassification, risk of demographic bias, transparency) were identified through model evaluation, fairness testing, human review