This post introduces EPIC's exploration of actionable recommendations and points of agreement from leading A.I. frameworks, beginning with the National Institute of Standards and Technology's AI Risk Management Framework.
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.
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.
In this interview, Code for America staff members share how client success, data science, and qualitative research teams work together to consider the responsible deployment of artificial intelligence (AI) in responding to clients who seek assistance with three products.
This benefits protection toolkit is a step-by-step guide to develop and integrate a benefits protection strategy into your Direct Cash Transfer (DCT) program design. This toolkit includes a set of customizable templates, letters, and other tools which should be downloaded and modified for your pilot and local context.
In June 2022, Legal Aid of Arkansas won a significant victory in their ongoing work to compel Arkansas’ employment agency to disclose crucial details about how it uses automated decision-making systems to detect and adjudicate fraud.
Directed at government practitioners, this resource addresses common misconceptions about artificial intelligence (AI) and explains the current state of technology.
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 analyzes lawsuits that have been filed within the past 10 years arising from the use of algorithm-driven systems to assess people’s eligibility for, or the distribution of, public benefits. It identifies key insights from the various cases into what went wrong and analyzes the legal arguments that plaintiffs have used to challenge those systems in court.
The primer–originally prepared for the Progressive Congressional Caucus’ Tech Algorithm Briefing–explores the trade-offs and debates about algorithms and accountability across several key ethical dimensions, including fairness and bias; opacity and transparency; and lack of standards for auditing.
