AI Impact Assessment

A formal, documented process (using the supplied checklist) to evaluate privacy, fairness, security, accuracy and reputational risks posed by an AI system before deployment; it identifies potential harms, mitigation measures, residual risks and informs go/no‑go and control decisions by firms.

A written assessment (also referenced as DPIA/AI Impact Report) that identifies and evaluates the potential risks, harms and impacts of an AI system, assigns a risk classification (unacceptable, high, limited, insignificant), and documents proposed mitigation measures and compliance steps required for registration and certification.

A documented, structured assessment of an AI system's potential harms, risks and impacts on rights, safety, fairness and privacy, used to identify mitigation, monitoring and governance measures. The Framework recommends mandatory AI impact assessments for high-risk use-cases and provides for templates and public reporting (with privacy-protecting redactions).

A documented assessment used to identify and evaluate the risks associated with an AI system, record mitigation measures, supply‑chain considerations, and governance controls, and to support regulator engagement, transparency and assurance processes as set out in the White Paper.

A structured, mandatory assessment produced prior to deployment for high‑risk AI systems that identifies potential harms, measures likelihood and severity of impacts, documents data governance and mitigation measures, and records plans for monitoring and remediation. The AIA must be retained for supervisory review and updated during post‑market monitoring.

A systematic process to identify, evaluate, and mitigate potential risks and societal impacts of an AI system before its deployment. It covers ethical, legal, and social implications, ensuring proactive risk management.

AI Impact Assessments are mandatory evaluations performed both prior to and throughout the deployment of AI systems by federal agencies. These assessments are crucial for evaluating the intended purpose, expected benefits, performance of the AI model, and the ongoing impacts of its use on individuals and society, ensuring responsible and ethical AI deployment.

A systematic process for identifying, evaluating, and mitigating potential risks and negative impacts of AI systems on workers, including safety, privacy, and fairness, before and during their deployment.