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.
An emerging concern in algorithmic fairness is the tension with privacy interests. Data minimization can restrict access to protected attributes, such as race and ethnicity, for bias assessment and mitigation. This paper examines how this “privacy-bias tradeoff” has become an important battleground for fairness assessments in the U.S. government and provides rich lessons for resolving these tradeoffs.
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.
In this report, the UN Special Rapporteur critically examines uses of digital technologies for administration of welfare programs across international contexts, and makes recommendations for using technology responsibly and ethically.
This reporting explores how algorithms used to screen prospective tenants, including those waiting for public housing, can block renters from housing based on faulty information.
This report presents evidence on the use of algorithmic accountability policies in different contexts from the perspective of those implementing these tools, and explores the limits of legal and policy mechanisms in ensuring safe and accountable algorithmic systems.
