What it means
An algorithmic impact assessment examines purpose, affected people, benefits, harms, data, rights, alternatives, controls, and residual risk before a consequential AI use proceeds.
Why it matters
A late compliance checklist cannot reshape the design after procurement, data choices, automation level, and deployment commitments are fixed.
Practical focus
- Define the approved purpose, accountable owner, users, operating environment, and acceptable risk for algorithmic impact assessment before implementation or procurement
- Implement early and repeated assessment, stakeholder participation, rights and harm analysis, alternatives, mitigation ownership, approval, and reassessment triggers through documented architecture, policy, configuration, and change control
- Collect assessment versions, stakeholder input, identified harms, mitigations, residual-risk decisions, approvals, and postdeployment findings and compare the results with approved requirements, risk thresholds, and expected outcomes
- Exercise this failure scenario as a planned test: a high-impact system is approved using a generic template that never examines its actual users, decisions, and affected communities
Common mistakes
- Using the label algorithmic impact assessment without defining the exact system boundary, decision, data, and people in scope
- Treating a model response or vendor claim as sufficient evidence without testing the surrounding application and operating process
- Failing to preserve assessment versions, stakeholder input, identified harms, mitigations, residual-risk decisions, approvals, and postdeployment findings in a form that lets reviewers reconstruct decisions and changes
- Testing only the normal path and never rehearsing what happens when a high-impact system is approved using a generic template that never examines its actual users, decisions, and affected communities
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: