This research explores how software engineers are able to work with generative machine learning models. The results explore the benefits of generative code models and the challenges software engineers face when working with their outputs. The authors also argue for the need for intelligent user interfaces that help software engineers effectively work with generative code models.
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 article discusses the challenges of today’s centralized identity management and investigates current developments regarding verifiable credentials and digital wallets.
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)
This paper introduces a method for auditing benefits eligibility screening tools in four steps: 1) generate test households, 2) automatically populate screening questions with household information and retrieve determinations, 3) translate eligibility guidelines into computer code to generate ground truth determinations, and 4) identify conflicting determinations to detect errors.
The Playbook’s purpose is to guide researchers while supporting and lending authority to community organizations as they advocate for partnerships that will benefit their constituencies. The Playbook aims to provide some answers to such questions as: How can technologists and scientists engage communities in a spirit of partnership, without such extractive practices? How can community organizations work with researchers in ways that benefit their communities and expand their capacity, rather than burdening their staff?
Our existing maze of family tax benefits — including the CTC, Earned Income Tax Credit (EITC), Child and Dependent Care Tax Credit (CDCTC), and head of household (HoH) filing status — has several structural deficiencies that make overhauling the system a prerequisite for any effort to boost support for families with children. The report offers several options for expanding and streamlining family tax benefits to address these issues.
This report contributes to the quantitative measurement of psychological burdens by examining a case study of a single social program: the Supplemental Nutrition Assistance Program, by considering new quantitative measures of the psychological burdens faced by SNAP applicants.
Biometric identification technologies—such as facial recognition and fingerprinting—can affect underserved communities, including low-income and minority communities. GAO interviewed academics, advocacy groups, and technology experts to find out how.
