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 how despite unresolved concerns, an audit-centered algorithmic accountability approach is being rapidly mainstreamed into voluntary frameworks and regulations.
This guidebook offers an introduction to the risks of discrimination when using automated decision-making systems. This report also includes helpful definitions related to automation.
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.
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 project maps the challenges of constructing algorithmic impact assessments (AIAs) by analyzing impact assessments in other domains—from the environment to human rights to privacy and identifies ten needed components for a robust impact assessment.
Research examining how much poverty would decrease—overall, by age, and by race and ethnicity—and how much benefits would increase if all people eligible for safety net programs received the full benefits they qualify for in each of the 50 states and DC.
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.
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.
This report aims to help congressional leaders understand AI, and provide key terms and definitions that would be important in crafting or understanding potential legislation on the topic.
Little is known about how agencies are currently using AI systems, and little attention has been devoted to how agencies acquire such tools or oversee their use.
Directed at government practitioners, this resource addresses common misconceptions about artificial intelligence (AI) and explains the current state of technology.
