The guidelines for bias-free language contain both general guidelines for writing about people without bias across a range of topics and specific guidelines that address the individual characteristics of age, disability, gender, participation in research, racial and ethnic identity, sexual orientation, socioeconomic status, and intersectionality.
This paper introduces the problem of semi-automatically building decision models from eligibility policies for social services, and presents an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. There is enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.
This article examines how outdated state unemployment insurance (UI) systems struggled during the COVID-19 pandemic, leading to delays, technical failures, and widespread frustration for job seekers.
Government agencies adopting generative AI tools seems inevitable at this point. But there is more than one possible future for how agencies use generative AI to simplify complex government information.
GetYourRefund.org is an online portal by Code for America that helps low-income individuals claim thousands of dollars in tax credits, even after the traditional tax deadline has passed.
LA’MESSAGE is a one-way text messaging service developed by Code for America in partnership with Louisiana to broadcast reminders and guidance to residents enrolled in and eligible for SNAP, Medicaid, TANF, and WIC at key points throughout the benefits enrollment and renewal process.
The Policy2Code Prototyping Challenge explored utilizing generative AI technology to translate U.S. government policies for public benefits into plain language and code, culminating in a Demo Day where twelve teams showcased their projects for feedback and evaluation.
This case study examines how Michigan’s Department of Health and Human Services uses data practices to advance racial equity in child welfare through identity-informed data collection and anonymous decision-making.
U.S. Department of Health and Human Services (HHS)
