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 study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
This paper argues that a human rights framework could help orient the research on artificial intelligence away from machines and the risks of their biases, and towards humans and the risks to their rights, helping to center the conversation around who is harmed, what harms they face, and how those harms may be mitigated.
This study examines how individuals assess administrative burdens and how these views change over time within the context of the Special Supplemental Nutrition Assistance Program for Women, Infants, and Children (WIC).
This paper describes results from fieldwork conducted at a social services site where the workers evaluate citizens' applications for food and medical assistance submitted via an e-government system. These results suggest value tensions that result - not from different stakeholders with different values - but from differences among how stakeholders enact the same shared value in practice.
CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
In the article, researchers examines how administrative burdens in waitlist management for subsidized childcare in Massachusetts have led to significant reductions in the number of families awaiting assistance, potentially obscuring the true extent of unmet need.
This study investigates how administrative burdens influence differential receipt of income transfers after a family member loses a job, looking at Unemployment Insurance, Temporary Assistance for Needy Families, and the Supplemental Nutrition Assistance Program.
This article examines how administrative burdens in U.S. social safety net programs have changed over the past 30 years, showing that while average burdens have declined, inequality in who faces these burdens has grown.
The ANNALS of the American Academy of Political and Social Science
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)
Automated decision systems (ADS) are increasingly used in government decision-making but lack clear definitions, oversight, and accountability mechanisms.
