Topic: Audits + Accountability
-
Automation + AI 
AI Data Readiness Checklist
A data artificial intelligence (AI) checklist from the Commonwealth of Virginia.
-
Automation + AI 
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing
This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.
-
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 
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.
-
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 
Assembling Accountability: Algorithmic Impact Assessment for the Public Interest
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.
-
Automation + AI 
Algorithmic Accountability for the Public Sector
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.
-
Automation + AI
Algorithmic Accountability: A Primer
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.