This article discusses the various benefits of publicly-funded open-source software. These benefits include fairness and transparency, economic stimulus, and support of the Federal Source Code Policy Agenda.
Code for America discusses the importance of a people-centered, digital-first safety net. Tools of technology, policy, and good implementation can advance a bold vision that will allow the nation to push through the end of the COVID-19 crisis.
Code for America describes its work building the P-EBT online application and the consulting it provided to 10 states regarding implementing the program in a quick, effective, and human-centered way. Despite herculean efforts among human services and education agencies to get P-EBT off the ground, there were a few key technological, operational, and logistical barriers that consistently got in the way and hampered a smooth rollout of the program across the country.
The Technology Transformation Service at GSA recently created a new Public Benefits Studio to focus on fostering a more cohesive, coordinated experience for the public, across programs.
Defining a product in government digital services is crucial, as it serves as the means through which a service is delivered to the public, and understanding its attributes ensures effective and continuous improvement.
This analysis explores the dual nature of mobile state IDs, highlighting their potential to enhance digital identity verification while raising significant privacy and equity concerns.
Companies have been developing and using artificial intelligence (AI) for decades. But we've seen exponential growth since OpenAI released their version of a large language model (LLM), ChatGPT, in 2022. Open-source versions of these tools can help agencies optimize their processes and surpass current levels of data analysis, all in a secure environment that won’t risk exposing sensitive information.
This essay explains why the Center on Privacy & Technology has chosen to stop using terms like "artificial intelligence," "AI," and "machine learning," arguing that such language obscures human accountability and overstates the capabilities of these technologies.
