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What should an AI risk assessment cover?
An AI risk assessment is a structured review of how a system could help or harm people in a particular use context. The same model can present different risks depending on its users, affected people, data, task, deployment conditions, and the steps people take in response to its output. Assess the system and its surrounding workflow, including human decisions, connected services, and fallback procedures.
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NIST’s voluntary AI Risk Management Framework (AI RMF 1.0), released January 26, 2023, organizes this work into four functions:
- Govern: Set accountability, policies, and oversight across the AI lifecycle.
- Map: Establish the context, intended use, affected people, and potential impacts.
- Measure: Gather evidence about risks and system performance using appropriate methods.
- Manage: Prioritize risks, apply controls, make decisions, and monitor outcomes.
Govern applies across the work; Map, Measure, and Manage can be used for a particular system and lifecycle stage. NIST’s current framework page says the framework is being revised, so check that page for updates before relying on version details. The assessment process below uses the AI RMF as a practical guide, not as a legal requirement or a guarantee that a system is safe or fair.
How do you define scope and accountability?
Start by identifying precisely what is being assessed. “The chatbot” or “the prediction model” may be too broad: record the model or system version, relevant configuration, and specific deployment. An assessment of a pilot in one workflow does not automatically cover a later release, a different user group, or a new purpose.
- System and purpose: Name the system, version, intended task, expected benefit, and uses that are out of scope.
- People and setting: Identify intended users, the people whose data or circumstances affect its outputs, and where and how it will be used.
- Decision authority: State whether the AI recommends, ranks, generates, or makes a decision, and who has authority to act on its output.
- Lifecycle stage: Record whether the review concerns design, development, testing, deployment, or a material change to an existing system.
- Accountability: Name the assessment owner, incident handlers, approver of residual risk, and the person or role that can pause or roll back use.
These details define what evidence will be relevant and who must act if a risk becomes unacceptable.
How do you map potential harms and affected people?
Trace what happens from input to outcome. Describe what users provide, what the system produces, how people interpret or act on that output, and what happens when it is wrong, unavailable, or used in an unexpected way. Include foreseeable misuse and dependencies, such as a downstream service or a human review step that may fail under workload pressure.
For each plausible harm, identify the pathway and the people who may bear the consequences. Consider errors that deny access, expose information, mislead users, create unsafe instructions, or shift costs and burdens onto particular groups. Involve relevant domain experts and people likely to be affected where feasible; technical teams alone may not know which consequences matter most in the real workflow.
Rank #2
Keep different harm categories visible rather than compressing them into a single opaque score. A privacy exposure, a disparate error burden, and a safety hazard have different pathways and may require different evidence and remedies.
What should you test for bias, privacy, and safety?
Choose measurements to match the task, setting, and affected population. Explain why each test is relevant, what it can establish, and what it cannot. Data quality, test coverage, and context all limit the conclusions that can be drawn; an aggregate score can conceal meaningful differences.
| Risk area | Questions to assess | Evidence to examine |
|---|---|---|
| Bias and fairness | Which groups could face different error rates, access, burdens, or downstream outcomes? Could data or workflow choices perpetuate or amplify harmful bias? | Data coverage and quality; justified subgroup comparisons; error patterns and downstream effects; domain context explaining why selected comparisons matter. |
| Privacy | What personal or sensitive data is needed, who can access it, where does it travel, and could inputs or outputs expose it? | Data flows from collection and training through inference, logs, retention, sharing, and deletion; access controls; output exposure; incident detection and handling. |
| Safety | What hazards or misuse are foreseeable in this setting? How might the system fail, and can it fail safely outside its knowledge limits? | Validity and reliability of outputs; robustness and failure modes; harmful output pathways; monitoring, response times, escalation, and recovery. |
Assess bias in context
Ask which people could receive less accurate outputs, face barriers to access, bear greater review burdens, or experience worse downstream outcomes. Examine relevant data and workflow choices, not only model behavior: who is represented in the data, how labels were created, how outputs are used, and whether people can challenge consequential errors.
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Rank #3
Trace privacy across the data lifecycle
Map data from initial collection and model training through inference, logging, retention, sharing, and deletion. For each stage, record what information is necessary, who can access it, and how long it remains available. Consider whether a system’s inputs or outputs could expose personal or sensitive information, and how the organization would detect and handle an exposure.
NIST’s Generative Artificial Intelligence Profile, published July 26, 2024, includes generative-AI-specific actions for assessing privacy violations involving training data and related system risks. A privacy review is not proof of legal compliance: applicable duties depend on the jurisdiction and the specific processing.
Test safety and foreseeable misuse
Define hazards in the intended setting, including what could happen if a user misunderstands an output, the system gives invalid information, or someone deliberately tries to misuse it. Examine reliability, robustness, limits, and harmful output pathways. Check whether users can recognize uncertainty and whether the system can be escalated, restricted, or stopped when it is outside its knowledge limits.
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How do you test mitigations and failure handling?
A control is useful only if it works under the conditions in which the system will operate. Test safeguards, monitoring, human review, and fallback behavior with realistic users, inputs, workload, and dependencies. For generative AI, include attempts to circumvent safety measures and review outputs in the intended workflow.
- Check whether a person reviewing an output has the time, expertise, and authority to reject or correct it.
- Test how users can escalate a concern, report an incident, or obtain human assistance.
- Verify what the system does when required data or a dependency is unavailable, an output is uncertain, or a known limit is reached.
- Exercise pause, shutdown, rollback, and recovery procedures rather than assuming they will work.
- Record who responds, how quickly, and how an incident is investigated and resolved.
Where a test cannot reproduce real conditions or cover important users and inputs, record that limitation instead of treating a clean test result as proof of control effectiveness.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you record and decide on residual risk?
Maintain a risk register that makes each concern actionable. For every risk, record the harm, pathway, affected people, conditions that could trigger it, assumptions about likelihood and severity, available evidence, gaps, controls, owner, and residual risk after controls. Use separate entries where privacy, bias, or safety concerns have different causes or remedies.
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Before deployment or continued use, compare expected benefits with risks, costs, and tradeoffs. Decide which risks will be mitigated, accepted, transferred, or left unresolved; identify who approves each decision and what deployment constraints apply. Preserve the evidence, test limitations, sign-offs, and rationale so a later reviewer can understand why the decision was made.
How do you monitor the system after release?
Assessment continues after deployment because performance and consequences can change as data, users, workflows, and system behavior change. Monitor incidents and complaints, performance and data changes, distribution shifts, and whether controls remain effective. Make clear who reviews signals and who can act on them.
Set reassessment triggers for material changes to the model, data, prompts, user population, interface, or use. Also reassess when incidents, new evidence, or changed operating conditions challenge an earlier assumption. Tie each trigger to a response, such as investigating, adding controls, narrowing use, or pausing deployment.
How do you compare AI systems or deployment choices?
Compare real options against the same intended context and affected people. Include non-AI alternatives or different levels of automation where they are viable. NIST cautions that trustworthiness characteristics can trade off; a high result on one dimension does not settle whether an option is appropriate overall.
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| Comparison dimension | What to examine |
|---|---|
| Fit and impact | Match to the intended task; severity and likelihood of harm; affected groups; expected benefits and burdens. |
| Evidence and exposure | Validity, reliability, and robustness; evidence quality and limits; privacy exposure; subgroup outcomes where justified. |
| Control and recovery | Ability to detect and reverse errors; quality of human oversight; monitoring, escalation, and incident response; residual risk. |
| Practical tradeoffs | Costs and benefits, operational constraints, and input from interested parties. |
Do not rank options with a universal “responsible AI” score unless the scoring method is defined, justified, and suitable for the decision. Explain important tradeoffs and why the selected option is acceptable in its particular context.
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