This report explores the role that academic and corporate Research Ethics Committees play in evaluating AI and data science research for ethical issues, and also investigates the kinds of common challenges these bodies face.
Concerns over risks from generative artificial intelligence systems have increased significantly over the past year, driven in large part by the advent of increasingly capable large language models. But, how do AI developers attempt to control the outputs of these models? This primer outlines four commonly used techniques and explains why this objective is so challenging.
Center for Security and Emerging Technology (CSET)
Safeguarding privacy and addressing algorithmic bias can pose an under-recognized trade-off. This brief documents tradeoffs by examining the U.S. government’s recent efforts to introduce government-wide equity assessments of federal programs. The authors propose a range of policy solutions that would enable agencies to navigate the privacy-bias trade-off.
In this policy brief and video, Michele Gilman summarizes evidence-based recommendations for better structuring public participation processes for AI, and underscores the urgency of enacting them.
This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
What exactly are the differences between generative AI, large language models, and foundation models? This post aims to clarify what each of these three terms mean, how they overlap, and how they differ.
Center for Security and Emerging Technology (CSET)
There is a growing interest in the concept of digital wallets, but no generally accepted definition of the concept or its features. This systematic review examines prior studies to offer a definition of digital identity wallets.
Annual Computers, Software, and Applications Conference (COMPSAC)
This paper examines changes in administrative burden in U.S. social safety net programs, or the negative encounters with the state that people experience when trying to access and use the benefits for which they are eligible. While overall burdens have declined in most targeted programs, there is evidence of increasing inequality regarding who faces these burdens.
The ANNALS of the American Academy of Political and Social Science
The Digital Service Network (DSN) spoke with GC Forms’ Senior Product Manager Stevie-Ray Talbot and Acting Head Ioana Contu to learn more about the team's approach to building GC Forms.
This article explores some of the challenges gig workers face in enrolling in SNAP, as well as present and future policy solutions to ease access to SNAP.
