This 2023 presentation from the California Employment Development Department details the user personas the agency used to evaluate vendor proposals for a new identity proofing contract.
State of California Employment Development Department (EDD)
This scoring matrix from the California Employment Development Department (EDD) shows how the agency evaluated vendor proposals for a new identity proofing approach, based on vendors' ability to serve different user personas.
State of California Employment Development Department (EDD)
This piece highlights promising design patterns for account creation and identity proofing in public benefits applications. The publication also identifies areas where additional evidence, resources, and coordinated federal guidance may help support equitable implementations of authentication and identity proofing, enabling agencies to balance access and security.
This report provides specific actions the federal government could take to reduce identity fraud and establish a whole-of-government victim redress approach.
This case study describes how Nava is working with the General Services Administration, Fearless, and the U.S. Postal Service to design, build, and deliver a new in-person identity verification service to nearly 20,000 USPS locations
Hear perspectives on topics including centering beneficiaries and workers in new ways, digital service delivery, digital identity, and automation.This video was recorded at the Digital Benefits Conference (BenCon) on June 14, 2023.
Research from the Department of Labor shows that document management systems reduce barriers for claimants and help states be more efficient. With additional improvements and investment, these systems can be even more effective in serving the public and reducing backlogs in times of crisis.
In May 2020, Stanford's HAI hosted a workshop to discuss the performance of facial recognition technologies that included leading computer scientists, legal scholars, and representatives from industry, government, and civil society. The white paper this workshop produced seeks to answer key questions in improving understandings of this rapidly changing space.
This policy brief offers recommendations to policymakers relating to the computational and human sides of facial recognition technologies based on a May 2020 workshop with leading computer scientists, legal scholars, and representatives from industry, government, and civil society
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. This academic study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets.
