This article examines the concept of "viral cash" and suggests that the future growth of basic income programs will depend on advocacy networks rather than traditional policy diffusion across jurisdictions.
This resource provides examples and practical guides that explain how to use existing regulations and data sharing agreements to transfer client information or eligibility status between benefit programs.
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 article analyses ‘digital distortions’ in Rules as Code, which refer to disconnects between regulation and code that arise from interpretive choices in the encoding process.
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 found that using state-specific names for Medicaid programs increased confusion and reduced both positive and negative opinions about the program.
This article explores how legal documents can be treated like software programs, using methods like software testing and mutation analysis to enhance AI-driven statutory analysis, aiding legal decision-making and error detection.
Medicaid and SNAP have reduced racial and ethnic disparities in healthcare access and food security, but some administrative and eligibility policies continue to create inequitable barriers.
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).
Algorithmic impact assessments (AIAs) are an emergent form of accountability for organizations that build and deploy automated decision-support systems. This academic paper explores how to co-construct impacts that closely reflects harms, and emphasizes the need for input of various types of expertise and affected communities.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
