The Benefit Cliffs Calculator helps case managers and public benefit recipients to prepare for benefit cliffs (i.e., declines in benefits due to an increase in earnings). It compares the net resources and benefits available to families under different employment scenarios.
This study examines how individuals assess administrative burdens and how these views change over time within the context of the Special Supplemental Nutrition Assistance Program for Women, Infants, and Children (WIC).
This webpage from the U.S. Department of Labor (DOL) provides guidance on improving mobile usability for Unemployment Insurance (UI) systems to enhance customer experience and accessibility.
Research from the Department of Labor shows that document management systems reduce barriers for claimants and help states be more efficient. With additional improvements and investment, these systems can be even more effective in serving the public and reducing backlogs in times of crisis.
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)
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.
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. This academic study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets.
This report explores how despite unresolved concerns, an audit-centered algorithmic accountability approach is being rapidly mainstreamed into voluntary frameworks and regulations.
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.
This guide, directed at poverty lawyers, explains automated decision-making systems so lawyers and advocates can better identify the source of their clients' problems and advocate on their behalf. Relevant for practitioners, this report covers key questions around automated decision-making systems.
This post introduces EPIC's exploration of actionable recommendations and points of agreement from leading A.I. frameworks, beginning with the National Institute of Standards and Technology's AI Risk Management Framework.
