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.
The Governor's Office of Administration is seeking a digital director 2 to join our growing and innovative Commonwealth Office of Digital Experience PA team! This is your chance to work with a wide range of technologies for an organization that works for the common good of Pennsylvanians. If you are detail-oriented, have excellent communication skills, and want a career serving the public, we want to talk to you!
This GitHub repository includes resources that users of the UI wage data toolkit may find helpful. It covers a variety of topics, including equity, data security, programming, and data QC tips. It also serves as a place for our team to continue to post information that the TANF Data Collaborative (TDC) pilot sites found useful during our partnerships with them.
This study evaluates the use of RPA technology by three states to automate SNAP administration, focusing on repetitive tasks previously performed manually.
Ruling from the FCC granting the U.S. Department of Health and Human Services (HHS) to confirm that federal and state governmental agencies working in conjunction with local governments, governmental contractors, and managed care entities acting under contract with state governments may, under certain circumstances, make autodialed and prerecorded or artificial voice calls or send autodialed text messages to raise awareness of the eligibility and enrollment requirements for these governmental health care programs without violating the Telephone Consumer Protection Act (TCPA).
This toolkit is designed to assist state and local TANF agencies in accessing, linking, and analyzing employment data from unemployment insurance (UI) systems.
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
