These principles and best practices for AI developers and employers to center the well-being of workers in the development and deployment of AI in the workplace and to value workers as the essential resources they are.
These principles and best practices for AI developers and employers to center the well-being of workers in the development and deployment of AI in the workplace and to value workers as the essential resources they are.
The GAO placed the UI system on its High Risk List in June 2022, leading the DOL to develop a transformation plan detailing activities and strategies for building a resilient UI system capable of responding effectively to future economic challenges.
This webpage from the U.S. Department of Labor (DOL) provides guidance on improving mobile usability for Unemployment Insurance (UI) systems to enhance customer experience and accessibility.
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.
For the past year, modernization teams at the Department of Labor (DOL) have been helping states identify opportunities to automate rote, non-discretionary, manual tasks, with the goal of helping them speed up the time that it takes to process claims. This post provides more context on Robotic Process Automation (RPA) and potential use cases in unemployment insurance.
This U.S. Department of Labor (DOL) strategy outlines a roadmap for modernizing the Unemployment Insurance (UI) system to enhance efficiency, equity, and access for workers.
In the Fall of 2022, the USDOL Office of Unemployment Insurance Modernization (OUIM) consulted with the Illinois Department of Employment Security (IDES) to provide hands-on support with IT modernization and customer experience strategy. Primary discussions focused on making informed product, service, and business decisions based on qualitative and quantitative data— how might IDES leverage existing data streams to identify the most pressing technology issues in their unemployment insurance system, and how might IDES act upon this information in a timely and impactful manner?
