Chapter 09 · Sustainability & AISustainability & AI
Algorithmic impact assessment (AIA)
Definition
A structured, pre-deployment evaluation of an automated decision system's likely effects — on rights, equity, environment and operations — with mitigations and documentation proportional to risk.
References
This source provides part of the technical or institutional basis for the definition.
This source supports the explanation of how the term is applied, measured or governed in practice.
Overview
What it means
Canada operationalised the concept in 2019: its Directive on Automated Decision-Making makes a standardised AIA mandatory for federal systems, scoring impact level and triggering graduated safeguards. The idea now appears in the EU AI Act's fundamental-rights impact assessment and in many organisational AI policies.
How it is used
Teams complete AIAs before procurement or deployment, revisiting them as systems change; publication requirements (as in Canada) add external scrutiny. Scope increasingly includes environmental effects of AI deployments.
Why it matters
AIAs move AI governance upstream, before systems harden into operations. Done honestly they surface trade-offs early; done as box-ticking they manufacture reassurance — the difference lies in independence, publication and follow-up.