This article discusses the challenges of today’s centralized identity management and investigates current developments regarding verifiable credentials and digital wallets.
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.
It is frequently assumed that when rules are implemented as code, a rules engine is necessary. However, it is possible for policy people and engineers to effectively work together to code logic that drives technological system without needing a mediating rules engine at all.
FNS evaluations of Summer EBT programs show significant reductions in childhood food insecurity, but some eligible households do not fully redeem benefits.
The goal of the brief is to encourage policy makers and employers to consider benefits cliffs as they look to create mandatory wage increases, with a look at a legislative action in NYC.
The CUTGroup book explains how civic user testing (paying residents to test civic apps) can allow for more community engagement in civic tech. This book covers how to do UX testing, community engagement, and digital skills in one civic tech system.
This report investigates how D.C. government agencies use automated decision-making (ADM) systems and highlights their risks to privacy, fairness, and accountability in public services.
In May 2020, Stanford's HAI hosted a workshop to discuss the performance of facial recognition technologies that included leading computer scientists, legal scholars, and representatives from industry, government, and civil society. The white paper this workshop produced seeks to answer key questions in improving understandings of this rapidly changing space.
This report explores technologies that have the potential to significantly affect employment and job quality in the public sector, the factors that drive choices about which technologies are adopted and how they are implemented, how technology will change the experience of public sector work, and what kinds of interventions can protect against potential downsides of technology use in the public sector. The report categories technologies into five overlapping categories including manual task automation, process automation, automated decision-making systems, integrated data systems, and electronic monitoring.
