AI harm is a negative impact an AI system can cause or contribute to—not simply a model error. It may affect people who never use the system, communities, organizations, society, ecosystems, or the planet. Reducing that risk means assessing who could be affected, how serious the consequences could be, and whether safeguards work throughout design, deployment, use, and evaluation. NIST’s AI Risk Management Framework offers a voluntary way to organize that work; it cannot guarantee safety or replace applicable law.
What counts as an AI harm?
An error becomes a harm in context: who experiences it, what happens to them, and how severe the consequences are. A system can perform as designed and still contribute to a harmful outcome if its use, data, or effects disadvantage people or impose costs on others.
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The National Institute of Standards and Technology (NIST) frames AI impacts as potentially positive, negative, or both. For a potential negative event, risk depends on two things: the likelihood that it occurs and the magnitude of the resulting harm. That is a way to reason about risk, not a universal numerical score that makes every AI use comparable.
- People and groups: A system may produce unequal outcomes or represent people in ways that reinforce harmful bias.
- Communities and organizations: Effects can spread beyond an individual decision, changing how a service or institution treats people.
- Society and the environment: Wider effects can include accumulated social impacts and resource use associated with developing and operating systems.
- People who did not choose to use the system: Affected parties can include people whose information, circumstances, or communities are implicated by a system, even if they never interact with it.
These categories can overlap. Privacy risks may arise from data used to train a system; resource-intensive computing can have environmental implications; and security risks may involve confidentiality, integrity, availability, and the software or hardware beneath the AI system.
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How should an organization assess an AI risk?
Start with the consequences, not a generic label such as “biased” or “unsafe.” Describe a plausible event, the people or other entities it could affect, and the impact if it occurs. Then consider likelihood, severity, uncertainty, and whether the risk can be managed in the intended context.
- Impact: What could happen, how serious would it be, and who or what would bear the consequences?
- Likelihood and uncertainty: What evidence supports the estimate? What is unknown, or difficult to predict?
- Distribution: Could performance or consequences differ among populations, languages, or use contexts?
- Lifecycle stage: Where could the risk enter, and can a control prevent, detect, or reduce it?
- Manageability: Can the risk be reduced and monitored to an acceptable level, or does the use need to stop?
- Resource effects: Are computing or other environmental impacts material to this system and its use?
This is a practical set of questions derived from NIST’s risk framing, not a validated scoring scale. Record the evidence and assumptions behind an assessment rather than treating an estimate as certainty.
How can organizations reduce AI harms across the lifecycle?
NIST’s AI Risk Management Framework (AI RMF) is intended for developers, users, and evaluators. Its risk-management approach calls for attention across pre-design, design and development, deployment, use, and test and evaluation—not only at launch. The steps below turn that lifecycle principle into an operational process.
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Write down the system’s intended use, the task or decision it supports, who may be affected, foreseeable misuse, and the organization’s tolerance for negative impacts. Be specific about boundaries: a system suitable for one task or setting is not automatically suitable for another. Include indirect effects and people outside the user group.
2. Identify risks and assess their consequences
Describe potential harmful events and assess their likelihood and severity, including direct and indirect effects. Seek input from people who may be affected, and document assumptions, evidence gaps, and uncertainty. Account for the system’s limitations and the way the surrounding process may shape outcomes.
3. Test in the intended context
Evaluate the system for the actual use, relevant populations, and languages. Look for failure modes and meaningful differences in performance or consequences—not only average results. NIST’s AI Resource Center provides technical materials intended to help organizations operationalize testing, evaluation, verification, and validation.
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4. Choose safeguards and track residual risk
Match safeguards to the harm identified. Depending on the risk, a response may involve changing the system or its use, adding oversight or other controls, or declining to proceed. Record what risk remains after safeguards are applied, who is accountable for monitoring it, and what evidence would trigger a reassessment.
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Revisit the assessment when the system, its users, operating conditions, or use cases change. A launch review cannot settle risks for every later use: applications can evolve, and some impacts are difficult to assess in advance. Continued monitoring and evaluation make the risk process responsive to those changes.
6. Stop safely when risk is unacceptable
NIST says development and deployment should cease in a safe manner when negative risk reaches an unacceptable level—for example, when significant negative impacts are imminent, severe harms are occurring, or catastrophic risks are present. Resuming should depend on whether the risks can be sufficiently managed, not simply on whether the system is technically operational.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What harms need particular attention in generative AI?
NIST’s cross-sector Generative AI Profile (NIST AI 600-1), released July 26, 2024, complements the AI RMF with risks and suggested actions specific to generative AI. It highlights harmful bias, homogenization, and environmental impacts.
Bias, representation, and unequal performance
Generative systems can increase the speed and scale at which harmful bias manifests. The profile discusses representational harms and differences in model performance across demographic groups and languages. Those differences can contribute to discriminatory decisions or amplify existing social biases. Testing should therefore examine the relevant groups, languages, and use context rather than relying on a single aggregate result.
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Training, maintaining, and operating generative AI systems can be resource-intensive. Impacts depend on factors such as model activity, modality, hardware, and task. NIST’s 2024 profile says there is no agreed method to estimate generative AI’s environmental impacts, so a single emissions figure should not be treated as universal or settled.
To illustrate the scale sometimes discussed, the profile reports a NIST-attributed estimate that training a single transformer large language model can emit as much carbon as 300 round-trip flights between San Francisco and New York. This is a contextual comparison, not a universal emissions factor; the same profile notes the lack of an agreed estimation method.
What the NIST framework can—and cannot—do
NIST released AI RMF 1.0 on January 26, 2023, after an 18-month development process involving more than 240 contributing organizations, according to NIST’s AI Resource Center. NIST’s framework page says the AI RMF is being revised. Its Generative AI Profile was released in 2024 as a complement to the framework.
The framework is voluntary. NIST describes it as a tool for incorporating trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems. Those considerations include validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy; and fairness, with harmful bias managed.
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Using the framework does not prove that a system is safe, prevent every harmful outcome, or establish compliance with law. Legal and sector-specific requirements depend on the jurisdiction and use case. Organizations need to assess those obligations separately and keep responsibility for decisions, safeguards, and residual risks.
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