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 is a working list of plain language Spanish translations and recommended usage for common unemployment insurance terms. All content contained in this glossary has been tested and validated for readability and comprehension with Spanish speakers who have limited English proficiency.
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.
Artificial intelligence promises exciting new opportunities for the government to make policy, deliver services and engage with residents. But government procurement practices need to adapt if we are to ensure that rapidly-evolving AI tools meet intended purposes, avoid bias, and minimize risks to people, organizations, and communities. This report lays out five distinct challenges related to procuring AI in government.
This post introduces EPIC's exploration of actionable recommendations and points of agreement from leading A.I. frameworks, beginning with the National Institute of Standards and Technology's AI Risk Management Framework.
This guide provides a detailed overview summarizing the many initiatives and activities from Congress, the White House, federal agencies, and coalitions which may impact the digital identity landscape in the United States, including at state, local, Tribal, and territorial levels.
