VA Section 508 Office URL Ownership Prediction Model
Description
Inaccurate identification of URL ownership for webpages and documents leads to inefficiencies in remediation processes, delays in addressing issues, and gaps in monitoring and reporting. These issues ultimately undermine effective management and compromise the quality of electronic products for Veterans and their beneficiaries.
Detailed example
The AI Model has two outputs. 1) Agency Owner with responses as: VHA, VBA, NCA, VACO, OIT, OCTO, and Unknown. 2) Prediction Score ranging from 0 to 1. The system is comprised of manually executed scripts using the AI model on local GFE. Besides the AI model, the scripts additionally apply manually constructed business rules and create prepopulated excel files for each VA Administration to use for quarterly reviews and data entry.
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
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The AI system reduces the level of effort and turnaround time for the OIT 508 Office to curate URLs and coordinate with VA Administrations to review and, as necessary, remediate URLs for accessibility compliance. In the absence of the AI system both the 508 Office and VA Administrations would be required to undertake a lengthy and iterative process to identify the VA Administration that is the formal owner of a URL.
Controls / human review
ATO: Not reported; PIA: Not published