Organization: ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
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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.
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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.
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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.
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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.