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.
Algorithmic Impact Assessments (AIAs) are tools for organizational accountability in automated decision-support systems, but their effectiveness relies on accurately mapping “impacts” to real harms experienced by people.
The paper emphasizes that impacts are shaped through accountability relationships and warns that focusing only on convenient metrics can overlook actual harms; it recommends developing AIAs through cross-expertise consensus, including affected communities, and aligning measurable impacts with real-world harms for meaningful accountability.
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