Concern 225 · Working draft · editorial review in progress
Independent evaluation access
Limited independent evaluation access prevents outside researchers from testing important AI claims under realistic conditions.
Understanding the concern
Researchers may need stable access, sufficient usage limits, relevant documentation, and permission to conduct good-faith testing. Without these, public evidence can depend heavily on the developer's own choices. Selected access programs may help while still leaving questions about independence and coverage.
Unrestricted access is not always appropriate, especially where private data or dangerous capabilities are involved. The challenge is designing access that supports meaningful scrutiny with proportionate safeguards. Terms and enforcement should distinguish legitimate research from abuse rather than discouraging both together.
An example
A company invites inspection but permits only demonstrations it has prepared in advance.
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.
Evidence and its limits
The paper proposes legal and technical protections for good-faith evaluation. It is a proposal, not proof that every provider blocks research or must provide identical access.
Longpre and colleagues · 2024 · Research proposal
A Safe Harbor for AI Evaluation and Red Teaming ↗ (opens in a new tab)Proposes legal and technical protections for independent safety research; the proposal is not itself binding policy.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Can independent reviewers choose meaningful tests and report negative findings without undue interference?
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?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
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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