AI Impact Assessment

An AI Impact Assessment, also called an Algorithmic Impact Assessment or AIA, is a structured documentation and review process that evaluates the potential impacts of an AI system on individuals, groups, society, and the environment before and during deployment — analogous to a privacy impact assessment (PIA) or environmental impact assessment but specific to AI. Impact assessments are now required or strongly encouraged by the EU AI Act (Fundamental Rights Impact Assessment for high-risk systems), the NIST AI RMF (Map function), ISO 42001 (impact assessment control), the Canadian Directive on Automated Decision-Making (AIA tool with public scoring), the UK's Equality Impact Assessment guidance, and numerous US state and federal frameworks. A typical AIA covers system description (purpose, data, model, users), affected populations (who interacts with it, who is affected by its outputs), risk identification (accuracy, bias, security, privacy, environmental, human-rights), mitigation measures (controls, monitoring, human oversight), residual risk acceptance (who signs off), and post-deployment monitoring (what gets tracked, what triggers reassessment). Public-sector AIAs are often disclosed externally; private-sector AIAs are typically internal but increasingly disclosed in vendor reviews and procurement responses. Tooling is emerging — the Open AI Impact Assessment template from the Ada Lovelace Institute, the Canadian government's AIA scoring tool, commercial offerings from Credo AI, Holistic AI, and Fairly AI. A practical adoption recipe: define an AIA template aligned to your jurisdictional requirements, require completion before any AI system enters production, route assessments through an AI governance committee for sign-off, store completed AIAs in your AI inventory, and review them annually or upon material change. AI governance teams treat the AIA as the gateway document that converts AI ideation into governed deployment.

Impact assessment from 25 years of governance discipline: Centralpoint has supported impact-assessment workflows — privacy, security, accessibility, audience-fit — for 25 years across regulated clients. Extending that to AI impact assessment is incremental, not foundational, work. AIAs stay on-premise, tokens meter per skill, and AIA-governed chatbots deploy through one line of JavaScript.


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