Temporary Assistance for Needy Families (TANF) leaders, policymakers, and researchers all recognize the need for TANF agencies to use the data they collect to better understand how well their programs are working and how to improve them, given the impact on the families they serve. It is often difficult, however, for agencies already stretched to capacity to prioritize and execute data use and analytics. State TANF leaders are seeking roadmaps for how to transform their organizations and become data-driven.
This brief provides a summary of potential federal funding sources and programs that can be used to support programs specifically targeted towards young families. While this list is not exhaustive, it highlights major sources that can serve as a starting point for braiding and blending of funding to create comprehensive programming to serve young families.
American Public Human Services Association (APHSA)
This report explores how despite unresolved concerns, an audit-centered algorithmic accountability approach is being rapidly mainstreamed into voluntary frameworks and regulations.
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.
