Artificial Intelligence Risk Management Framework (AI RMF 1.0)¶
Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0).
Cited by¶
5 citations across 5 artifacts.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Machine-Learning Model
- NIST's AI Risk Management Framework therefore evaluates AI in context across design, development, use, and evaluation rather than treating a model file as the entire operational system.
This sourceFrames AI risk across design, development, deployment, use, evaluation, context, and lifecycle change.
Supported in partVerified against the publisher's abstract
The summary shows the framework targets organizations designing, developing, deploying or using AI, but is silent on context-based evaluation.
“organizations designing, developing, deploying, or using AI systems to help manage the many risks of AI”
- NIST's AI Risk Management Framework therefore evaluates AI in context across design, development, use, and evaluation rather than treating a model file as the entire operational system.
Mechanisms¶
- Dynamic Route Optimization
- The discipline is to keep a human able to override, weight the objective to include the externalities and burdens that matter, and validate against real outcomes, not just the model's own score.
This sourceCalls for human override mechanisms and evaluation under deployment-like and operational conditions rather than reliance on isolated technical measurements.
- The discipline is to keep a human able to override, weight the objective to include the externalities and burdens that matter, and validate against real outcomes, not just the model's own score.
- Fairness-Metric and Exception Stress Test
- The guarding discipline is to prioritize failure vectors by plausibility times severity — not by cleverness — and to keep the hardened rule explainable, remembering Goodhart's law:
This sourcePrioritizes risk treatment using likelihood and impact.
- The guarding discipline is to prioritize failure vectors by plausibility times severity — not by cleverness — and to keep the hardened rule explainable, remembering Goodhart's law:
- Hallucination Intrusion Triage
- Same flag severity from the detector; very different dispositions, chosen by consequence.
This sourceAI risk responses should use different treatments tailored and prioritized by projected impact, likelihood, context, and available resources.
- Same flag severity from the detector; very different dispositions, chosen by consequence.
- Signal Issuance Rubric
- The discipline that keeps it honest is to pair the rubric with after-the-fact review of firing rates and false alarms and to revise the bar when it drifts
This sourceCalls for false-positive and false-negative measurement, ongoing monitoring, and recalibration as deployed systems and data drift.
- The discipline that keeps it honest is to pair the rubric with after-the-fact review of firing rates and false alarms and to revise the bar when it drifts
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 1 of 5 citations: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
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Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
Registry ID ref:e49d5e27f2d9 · see in the full table