AI Risk Assessment: How to Evaluate Your AI Systems
An AI risk assessment (or algorithmic impact assessment) is the process of identifying, evaluating, and mitigating the risks posed by an AI system before and during deployment. Here is how to do it right.
- 5
- risk dimensions in a complete AIA
- 3
- risk tiers (TRAIGA)
- Required
- for high-risk systems under TRAIGA
- Auto
- risk scoring in TRAIGA platform
What Is an AI Risk Assessment?
An AI risk assessment (also called an algorithmic impact assessment or AIA) is a structured analysis of the risks an AI system poses across multiple dimensions: accuracy, bias, data privacy, security, transparency, and legal compliance.
The goal is to identify risks before they cause harm, and to document the mitigation measures in place. Regulators use AIA documentation to evaluate whether deployers have exercised due care in their AI deployment decisions.
The Five Risk Dimensions in an AI Assessment
A complete AI risk assessment evaluates five dimensions:
- Accuracy and Reliability — how often does the system produce correct outputs? What is the error rate under distribution shift? Have accuracy claims been independently validated?
- Algorithmic Bias and Fairness — does the system produce systematically different outcomes for different demographic groups? Has disparity been quantified and mitigated?
- Data Privacy — what personal or sensitive data does the system process? Is processing compliant with applicable privacy law? Are data minimization and purpose limitation principles applied?
- Security and Adversarial Robustness — can the system be manipulated through adversarial inputs? Are model outputs exploitable for malicious purposes?
- Transparency and Explainability — can the system's outputs be explained to affected individuals and regulators? Does the level of explainability match the decision stakes?
Regulatory Requirements for AI Risk Assessments
Multiple AI regulations now require formal AI risk assessments or algorithmic impact assessments as a legal prerequisite for deployment.
- SB 1964 / 1 TAC Chapter 219 (Texas) — requires Texas governmental entities to conduct § 219.22 risk assessments and § 219.23(b) impact assessments for Heightened Scrutiny AI (HSAI) systems. For private-sector deployers under TRAIGA, impact assessments are best practice rather than a statutory mandate.
- Colorado Artificial Intelligence Act — Section 6-1-1703 requires deployers to complete an impact assessment for high-risk AI systems before deployment.
- NIST AI RMF — the Measure function requires organizations to analyze, assess, and track AI risks using quantitative and qualitative approaches.
Frequently asked questions
What is the difference between an AI risk assessment and an algorithmic impact assessment?
The terms are often used interchangeably. 'Algorithmic impact assessment' (AIA) tends to emphasize civil rights and disparate impact dimensions, while 'AI risk assessment' is broader and includes accuracy, security, and legal compliance dimensions. TRAIGA (Texas) and Colorado law use 'algorithmic impact assessment'.
How often should an AI risk assessment be repeated?
Regulations typically require periodic reassessment — at minimum annually, and whenever there is a significant change to the AI system, its training data, its deployment context, or the applicable regulatory environment.
Do I need an AI risk assessment for all AI systems?
Not all regulations require formal AIAs for all AI systems. SB 1964 / 1 TAC 219 requires risk and impact assessments for Heightened Scrutiny AI (HSAI) systems deployed by Texas governmental entities. TRAIGA does not mandate AIAs for private organizations, but best practice is to conduct at least a basic risk screening for every AI system, with a full AIA for systems that score Moderate or High risk.
Can TRAIGA run the AI risk assessment for me?
TRAIGA includes a structured AIA workflow. You answer a guided questionnaire for each AI system, and the platform auto-scores risk tier, generates a control set, and produces an AIA documentation pack suitable for regulatory submission.
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