Concern 217 · Working draft · editorial review in progress
No mandatory incident reporting
Gaps in mandatory incident reporting can keep AI failures from reaching regulators, researchers, or other organizations that could learn from them.
Understanding the concern
If reporting is voluntary or narrowly defined, a harmful event may remain inside the organization involved. Without shared information, similar mistakes can recur and the public may see an incomplete picture of risk. Useful reports also need enough detail to distinguish causes and consequences.
It is inaccurate to say that no reporting duties exist. Some sectoral and AI-specific regimes require reports for covered incidents. Coverage, thresholds, recipients, and timing differ, so the question is which harms fall between those rules and how reports protect sensitive information.
An example
Several organizations encounter the same serious defect, but each assumes it is an isolated problem because no shared record exists.
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 sources show both reporting frameworks and jurisdiction-specific duties; they do not support a blanket claim that reporting is absent.
OECD · February 2025 · Intergovernmental policy framework
Towards a common reporting framework for AI incidents ↗ (opens in a new tab)Proposes common incident-reporting criteria adaptable to domestic frameworks; not itself a mandatory reporting law.
European Commission AI Act Service Desk · Legislative explanation
Article 73: Reporting of serious incidents ↗ (opens in a new tab)Reporting structure for serious high-risk-system incidents. Exact duties depend on system, applicable law, amendments and effective date.
Office of the Governor of California · September 2025 · Official legislation announcement
Governor Newsom signs SB 53 ↗ (opens in a new tab)Describes frontier-model transparency, safety-incident and whistleblower provisions; the statute controls detailed coverage and procedure.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Which incidents must be reported, which may remain invisible, and who can learn from the reports?
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.
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