Described as the “public’s one account for government,” this U.S. government website allows users to use one account and password for secure, private access to participating government agencies.
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.
Remote identify proofing is the process federal agencies and other entities use to verify that the individuals who apply online for benefits and services are who they claim to be. If the applicant responds correctly to personal questions, their identity is considered to be verified. However, data stolen in recent breaches could be used fraudulently to respond to knowledge-based verification questions. Alternative methods are available that provide stronger security, but these methods may have limitations in cost, convenience, technological maturity, and they may not be viable for all segments of the public.
This report from the Joint Financial Management Improvement Program outlines efforts to use identity verification to reduce improper payments in government programs, while mitigating bias and disparate impacts.
The Joint Financial Management Improvement Program (JFMIP)
This program letter from the Employment and Training Administration Advisory System, U.S. Department of Labor to State Workforce Agencies highlights the importance of identity verification in ensuring the proper payment of unemployment benefits and provide guidance to states on required administrative procedures.
Employment and Training Administration Advisory System
These guidelines provide technical requirements for federal agencies implementing digital identity services and are not intended to constrain the development or use of standards outside of this purpose. This guideline focuses on the enrollment and verification of an identity for use in digital authentication.
National Institute of Standards and Technology (NIST)
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.
