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.
This charter defines the goals, scope and organization of the “Integrated Service Delivery (ISD) Product Office” charged with planning, implementing, governing, and managing all business transformation, change, and systems modernization efforts related to integrated service delivery, focused initially on integrated eligibility and enrollment.
This paper argues that a human rights framework could help orient the research on artificial intelligence away from machines and the risks of their biases, and towards humans and the risks to their rights, helping to center the conversation around who is harmed, what harms they face, and how those harms may be mitigated.
2022 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization
Through a field scan, this paper identifies emerging best practices as well as methods and tools that are becoming commonplace, and enumerates common barriers to leveraging algorithmic audits as effective accountability mechanisms.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
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.
On December 5, 2022, an expert panel, including representatives from the White House, unpacked what’s included in the AI Bill of Rights, and explored how to operationalize such guidance among consumers, developers, and other users designing and implementing automated decisions.
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.
Automated decision systems (ADS) are increasingly used in government decision-making but lack clear definitions, oversight, and accountability mechanisms.
This post argues that for the types of large-scale, organized fraud attacks that many state benefits systems saw during the pandemic, solutions grounded in cybersecurity methods may be far more effective than creating or adopting automated systems.
This report by EPIC investigates how automated decision-making (ADM) systems are used across Washington, D.C.’s public services and the resulting impacts on equity, privacy, and access to benefits.
