AI Impact Assessment
Documented risk and harms analysis before AI deployment.
How the laws define this
Every cited framework defines an AI Impact Assessment as a structured, documented process to identify, evaluate, and mitigate potential risks, harms, and safety or privacy impacts posed by an AI system. Beyond this core agreement, the instruments diverge on timing and operational scope. The Nigerian Bar Association Guidelines for the Use of Artificial Intelligence in the Legal Profession, Expediente 2505-D-2023, and An act relating to creating oversight and liability standards for developers and deployers of inherently dangerous artificial intelligence systems focus on evaluations conducted before deployment. Conversely, OMB Memorandum M-25-21: Accelerating Federal Use of AI through Innovation, Governance, and Public Trust and OSHA Guidance on Artificial Intelligence in Workplace Safety mandate assessments both prior to and during deployment. Furthermore, the National Artificial Intelligence Policy Framework (Draft) — Department of Communications and Digital Technologies and Government bill, Senate 442/25 limit mandatory assessments specifically to high-risk AI systems.
Synthesised from the 8 statutory definitions below. Each one is quoted in full, with its source.
How jurisdictions differ
What is distinctive in each definition, binding law in force first. Showing 7 of the 8 instruments that define this term; all are listed under Definitions below.
| Jurisdiction | Instrument | Status | What is distinctive |
|---|---|---|---|
| United States | OSHA Guidance on Artificial Intelligence in Workplace Safety | In Force | Limits scope to workplace safety, targeting risks to workers including health impacts from AI integration. |
| United Kingdom | A Pro‑Innovation Approach to AI Regulation (White Paper) | Adopted | Includes supply-chain considerations and governance controls alongside risks, emphasizing regulator engagement and transparency. |
| Nigeria | Nigerian Bar Association Guidelines for the Use of Artificial Intelligence in the Legal Profession | Adopted | Uses a supplied checklist focused on legal profession risks like privacy and reputational harm, informing firm go/no-go decisions. |
| Argentina | File 2505-D-2023 – Research, Development and Use of Artificial Intelligence | Proposed | Assigns explicit risk classifications (unacceptable, high, limited, insignificant) and links to mandatory registration and certification. |
| Vermont, United States | An act relating to creating oversight and liability standards for developers and deployers of inherently dangerous artificial intelligence systems | Withdrawn | Explicitly covers ethical, legal, and social implications beyond technical risks. |
| Colombia | By means of which artificial intelligence is regulated in Colombia to guarantee its ethical and responsible development | Stalled | Mandates structured assessments for high-risk systems only, with retention for supervisory review and post-market updates. |
| South Africa | National Artificial Intelligence Policy Framework (Draft) — Department of Communications and Digital Technologies | Superseded | Recommends templates and public reporting with privacy-protecting redactions, specifically for high-risk use-cases. |
What it triggers
- Federal agencies are directed to perform AI Impact Assessments prior to and throughout deployment of AI systems.source
- In Colombia's proposed bill, high-risk AI systems would require a structured assessment prior to deployment, retained for supervisory review and updated during post-market monitoring.source
- Argentina's proposed bill would require a written assessment for AI systems to classify risks and document compliance steps for registration and certification.source
Definitions (8)
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.
Not to be confused with
Ethical Impact Assessment focuses solely on ethical risks and benefits, while AI Impact Assessment covers broader risks including safety, privacy, and fairness.
Impact analysis broadly assesses effects on rights, equity, security, and public value, whereas AI Impact Assessment specifically evaluates and mitigates AI system risks pre-deployment.
Questions and answers
Do AI Impact Assessments apply only before deployment?
Most definitions focus on pre-deployment, but OMB M-25-21 requires ongoing assessments throughout deployment, and Colombia's bill includes post-market updates.
What happens if an AI system poses unacceptable risks?
Argentina's bill classifies it as unacceptable, requiring mitigation measures and compliance steps for registration and certification.
Related Terms
AI Impact Assessment (AIA)
Pre‑deployment assessment of risks and mitigation measures....
Ethical Impact Assessment
Pre-deployment assessment of ethical risks and benefits....
AI Impact Assessment (AIIA)
A systematic process to identify, evaluate, and mitigate AI system risks and impacts....
Impact analysis
Structured assessment of effects on rights, equity, security and public value....
AI system impact assessment (AIIA)
A repeatable assessment of an AI system's impacts across its lifecycle....