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)
This UN report warns against the risks of digital welfare systems, emphasizing their potential to undermine human rights through increased surveillance, automation, and privatization of public services.
Code for America CEO introduces the Safety Net Innovation Lab in a TED Talk, their initiative to work with state governments to reimagine and rebuild delivery of accessible and equitable benefits. This article also includes the video of Renteria’s talk and a transcript.
This study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
The primer–originally prepared for the Progressive Congressional Caucus’ Tech Algorithm Briefing–explores the trade-offs and debates about algorithms and accountability across several key ethical dimensions, including fairness and bias; opacity and transparency; and lack of standards for auditing.
Drawing on interviews and convenings with experts and practitioners from the field of public interest technology, this report contains recommendations across five core priority action areas for cross-sector innovation and collaboration to improve state benefits systems through procurement practices.
This report outlines how the New Jersey Department of Human Services’ Division of Family Development (DFD) and the Department of Health (NJDOH) are increasing SNAP & WIC co-enrollment through data sharing, outreach, and systems integration.
American Public Human Services Association (APHSA)
This report recommends updating the methodology used by the Census Bureau to calculate the Supplemental Poverty Measure (SPM) to reflect household basic needs and replace the current Official Poverty Measure as the primary statistical measure of poverty. The report assesses the strengths and weaknesses of the SPM and provides recommendations for updating its methodology and expanding its use in recognition of the needs of most American families such as medical care, childcare, and housing costs.
National Academies of Sciences, Engineering, and Medicine
Sharing lessons learned via the Medicaid Churn Learning Collaborative, which is working to reduce Medicaid churn, improve renewal processes for administrators, and protect health insurance coverage for children and families.
