SSA · no agency ID · record R3104

Quick Disability Determinations Model

Social Security Administration · Law and Policy, Disability Policy

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: 2008-02-01 (day precision)Source: 2/1/2008 0:00
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Government Benefits Processing
Operational functions: Claims and case processing Derived by keyword rules; see methodology

Problem the AI is intended to solve

Used to screen initial applications to identify cases where a favorable disability determination is highly likely and medical evidence is readily available to prioritize this workload and expedite case processing.

Expected benefits

QDD predictive model quickly identifies and prioritizes disability claims processing for individuals with severe, life-threatening, or life-altering conditions. This approach reduces processing times, improves access to benefits for those in urgent need, and allows the agency to allocate resources more efficiently, ultimately enhancing service delivery for the public.

System outputs

System output consists of probability model scores for each separate scoring service, Scoring Service for Allowance and Scoring Service for Processing Time, and the overall QDD Score.

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): eDIB (electronic disability)
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Purchased from vendorSource: a) Purchased from a vendor
Vendor (as reported): IBM
Vendors (standardized): IBM

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: Yes
Demographic features: b) Sex; c) Age
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 processReported in placeSource: a) Yes, an appropriate appeal process has been established

User and public consultationOther or ambiguous answerSource: d) Other

Potential impacts and how they were identified

Positive Impacts - Expedited processing of disability applications & DDS prioritization of straightforward cases resulting in faster overall processing time Negative Impacts - false negatives, rights impacting, resource impacting