Concern 922 · Working draft · editorial review in progress
Model-risk committees
Model-risk committees can miss important AI harms if they focus only on technical performance and leave real-world use outside their review.
Understanding the concern
A model can pass a benchmark yet be unsuitable for a decision, population or workflow. Review needs input from people who understand the task and the consequences of mistakes, as well as the model itself. Teams also need a way to revisit approval after updates or changes in use.
Traditional model-risk practices provide useful questions, but their scope should not be overstated. US banking guidance SR 26-2 excludes generative and agentic AI, so organizations need to identify the appropriate additional approach. Think of approving a vehicle's engine without checking the route or driver. A committee should examine the system in context and record the limits of its approval.
An example
A committee approves a model's average performance while nobody reviews how staff will use its answers in consequential cases.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Agent
- An AI system that can choose and carry out several steps toward a goal, using the tools and access people give it.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- Benchmark
- A standard test used to compare systems. It only measures the tasks and conditions included in that test.
Evidence and its limits
The banking guidance is scoped and excludes generative and agentic AI. The broader committee design described here is a governance proposal, not a claim that SR 26-2 mandates it.
Federal Reserve, FDIC and OCC · April 2026 · Official supervisory guidance
Supervisory Guidance on Model Risk Management: SR 26-2 attachment ↗ (opens in a new tab)Replaces prior guidance; explicitly excludes generative and agentic AI models from scope and does not set enforceable standards by itself.
NIST · AI 600-1 · 2024 · Official risk framework
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile ↗ (opens in a new tab)Describes generative-AI risk categories and management actions; it does not measure the failure rate of every product.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- NIST AI RMF (opens in a new tab)
framework
- ISO/IEC 42001 (opens in a new tab)
standard
A question to discuss
Does this review cover the actual system and use, or only a narrow technical component?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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