APHSA explains how certain tools and recommendations about when people apply for help, engage in services, and maintain benefits can have a powerful effect to either counter or exacerbate structural barriers to accessing assistance.
American Public Human Services Association (APHSA)
This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
The U.S. Department of Labor is working with states, territories, and the public to develop strategies to continuously improve the nation’s unemployment insurance (UI) systems.
The Center on Budget and Policy Priorities (CBPP) report discusses how reducing administrative burdens in Medicaid can enhance health outcomes and promote racial equity.
This primer introduces two foundational software types that can support organizations that are committed to accessible benefits information: content management systems (CMS) and application program interfaces (APIs).
This kit contains a collection of styles, components, and building blocks to quickly create action-forward emails for Unemployment Insurance programs within the U.S.
The guidelines for bias-free language contain both general guidelines for writing about people without bias across a range of topics and specific guidelines that address the individual characteristics of age, disability, gender, participation in research, racial and ethnic identity, sexual orientation, socioeconomic status, and intersectionality.
Better Rules utilizes multidisciplinary teams that include people skilled in policy, legal, business rules, programming, and service design working together in an iterative fashion to develop rules. Several outputs are produced using this approach, each offering an opportunity that can be fed back into that iterative process and re-used to solve other issues.
This Urban Institute article argues that poverty is driven by structural barriers rather than individual choices and advocates for safety net programs that address systemic inequities.
This academic paper examines how federal privacy laws restrict data collection needed for assessing racial disparities, creating a tradeoff between protecting individual privacy and enabling algorithmic fairness in government programs.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
This report explores how despite unresolved concerns, an audit-centered algorithmic accountability approach is being rapidly mainstreamed into voluntary frameworks and regulations.
