For the past year, modernization teams at the Department of Labor (DOL) have been helping states identify opportunities to automate rote, non-discretionary, manual tasks, with the goal of helping them speed up the time that it takes to process claims. This post provides more context on Robotic Process Automation (RPA) and potential use cases in unemployment insurance.
The OECD AI Principles promote use of AI that is innovative and trustworthy and that respects human rights and democratic values. The principles were adopted in 2019; this webpage provides an overview of the principles and key terms.
Organisation for Economic Co-operation and Development (OECD)
This study evaluates the use of RPA technology by three states to automate SNAP administration, focusing on repetitive tasks previously performed manually.
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
This playbook provides federal agencies with guidance on implementing AI in a way that is ethical, transparent, and aligned with public trust principles.
U.S. Department of Health and Human Services (HHS)
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)
Automated decision systems (ADS) are increasingly used in government decision-making but lack clear definitions, oversight, and accountability mechanisms.
This report explores how AI is currently used, and how it might be used in the future, to support administrative actions that agency staff complete when processing customers’ SNAP cases. In addition to desk and primary research, this brief was informed by input from APHSA’s wide network of state, county, and city members and national partners in the human services and related sectors.
American Public Human Services Association (APHSA)
NIST has created a voluntary AI risk management framework, in partnership with public and private sectors, to promote trustworthy AI development and usage.
National Institute of Standards and Technology (NIST)
This report explores how despite unresolved concerns, an audit-centered algorithmic accountability approach is being rapidly mainstreamed into voluntary frameworks and regulations.
