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 resource is a research paper examining the role of the public safety net in insuring job losers against income loss, analyzing which government programs provide financial support and how benefits vary based on pre-job loss income levels.
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.
The exclusion of agricultural and domestic workers—predominantly African Americans—from the 1935 Social Security Act's unemployment insurance program is analyzed as a result of international policy diffusion rather than solely domestic racial politics.
This study examines how bureaucratic interactions differ among public assistance programs—WIC, SNAP, and Medicaid—highlighting variations in participant experiences and the psychological costs associated with each.
This article explores how anticipatory logics—drawing from foresight, futures thinking, and design—are shaping the future of government by creating space for innovative policy approaches, public participation, and proactive governance.
This research paper examines how stigma shapes participation in U.S. social safety net programs and influences public support for benefit design and access.
This article examines the historic structural changes to the Supplemental Nutrition Assistance Program (SNAP) enacted through H.R. 1 and their potential consequences for public health and food security.
