Library
Discover the latest innovations, learn about promising practices, and find out what’s coming next with best-in-class resources from trusted sources.
Is there something missing from our library?
Search for the topic or resource you're looking for, or use the filters to narrow down results below.
-
Automation + AI Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts
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.
-
Medicaid Strategies Making a Difference: A Spotlight on Rhode Island
Sharing lessons learned via the Medicaid Churn Learning Collaborative, which is working to reduce Medicaid churn, improve renewal processes for administrators, and protect health insurance coverage for children and families.
-
Automation + AI Who Audits the Auditors? Recommendations from a Field Scan of the Algorithmic Auditing Ecosystem
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.
-
Automation + AI Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. This academic study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets.
-
Automation + AI The Social Life of Algorithmic Harms
This series of essays seeks to expand our vocabulary of algorithmic harms to help protect against them.
-
Digital Identity Regulating Biometrics: Taking Stock of a Rapidly Changing Landscape
This post reflects on and excerpts from AI Now's 2020 report on biometrics regulation.
-
Automation + AI The Privacy-Bias Tradeoff: Data Minimization and Racial Disparity Assessments in U.S. Government
An emerging concern in algorithmic fairness is the tension with privacy interests. Data minimization can restrict access to protected attributes, such as race and ethnicity, for bias assessment and mitigation. This paper examines how this “privacy-bias tradeoff” has become an important battleground for fairness assessments in the U.S. government and provides rich lessons for resolving these tradeoffs.
-
Automation + AI Algorithmic Accountability: Moving Beyond Audits
This report explores how despite unresolved concerns, an audit-centered algorithmic accountability approach is being rapidly mainstreamed into voluntary frameworks and regulations.
-
Automation + AI Access Denied: Faulty Automated Background Checks Freeze Out Renters
This reporting explores how algorithms used to screen prospective tenants, including those waiting for public housing, can block renters from housing based on faulty information.
-
Policy A Safety Net with 100 Percent Participation: How Much Would Benefits Increase and Poverty Decline?
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.
-
Automation + AI Digital Welfare States and Human Rights
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.
-
Automation + AI POVERTY LAWGORITHMS: A Poverty Lawyer’s Guide to Fighting Automated Decision-Making Harms on Low-Income Communities
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.