This academic article develops a framework for evaluating whether and how automated decision-making welfare systems introduce new harms and burdens for claimants, focusing on an example case from Germany.
Automated decision systems (ADS) are increasingly used in government decision-making but lack clear definitions, oversight, and accountability mechanisms.
This primer is written for a non-technical audience to increase understanding of the terminology, applications, and difficulties of evaluating facial recognition technologies.
Recording of GOVChats hosted by GTA's Digital Services Georgia, where speakers dive into the artificial intelligence (AI) programs and initiatives unfolding across the states of Georgia, Maryland, and Vermont.
This report explores how despite unresolved concerns, an audit-centered algorithmic accountability approach is being rapidly mainstreamed into voluntary frameworks and regulations.
This report provides an overview of artificial intelligence (AI), key policy considerations, and federal government activities related to AI development and regulation.
This research explores how software engineers are able to work with generative machine learning models. The results explore the benefits of generative code models and the challenges software engineers face when working with their outputs. The authors also argue for the need for intelligent user interfaces that help software engineers effectively work with generative code models.
A training course on using artificial intelligence (AI) tools to de-jargonize government language, with a tutorial on turning a complex piece of government writing into simpler and easier-to-understand language for government employees and residents alike.
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 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 resource helps individuals with aligning their work with the needs of the communities they wish to serve, while reducing the likelihood of harms and risks those communities may face due to the development and deployment of AI technologies.
