This paper concludes that the substantial COVID-19 unemployment insurance expansion had limited disincentive effects on job searches, particularly among lower-income individuals, despite high wage replacement rates.
This article explores how AI and Rules as Code are turning law into automated systems, including how governance focused on transparency, explainability, and risk management can ensure these digital legal frameworks stay reliable and fair.
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.
Through our research understanding the government digital service field and what workers in this field need, we want to help strengthen those existing roles and establish more pathways for promotion and career support, as well as help other teams recognize the value of these skills and create new roles.
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 brief analyzes the current state of federal and state government communication around benefits eligibility rules and policy and how these documents are being tracked and adapted into code by external organizations. This work includes comparisons between coded examples of policy and potential options for standardizing code based on established and emerging data standards, tools, and frameworks.
This study explores the causal impacts of income on a rich array of employment outcomes, leveraging an experiment in which 1,000 low-income individuals were randomized into receiving $1,000 per month unconditionally for three years, with a control group of 2,000 participants receiving $50/month.
