Concern 898 · Working draft · editorial review in progress
Third-party AI risk
Third-party AI risk concerns the limits of relying on an outside supplier's claims, controls and ongoing service.
Understanding the concern
A buyer may receive a questionnaire, certification or impressive benchmark without enough evidence for its own use. The supplier's tests may involve different data or users, and the product may change after purchase. Responsibility for the customer's workflow cannot be understood from a sales demonstration alone.
Procurement guidance encourages examining the system across its life, including data handling and contractual rights. Think of hiring a contractor: a good reference is helpful, but the actual job still needs clear requirements and inspection. Buyers should ask what evidence they can obtain, how changes are reported and who responds to a problem. The depth of review should match the stakes.
An example
A vendor's general accuracy claim is used to approve a high-stakes deployment without testing the buyer's real 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.
- Benchmark
- A standard test used to compare systems. It only measures the tasks and conditions included in that test.
Evidence and its limits
Government procurement guidance supplies principles, not a certification of any vendor or a guarantee that a completed questionnaire is sufficient assurance.
UK Government · Procurement guidance
Guidelines for AI procurement ↗ (opens in a new tab)Evaluating need, suppliers, data, risk and lifecycle responsibilities. Contract terms must match the actual purchase.
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
Which supplier claims have we checked for our setting, and what continuing evidence can we require?
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.
About the register and scoring →More ways to explore
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