SunAccord: Agreement on AI Harms, Risks, and Disagreements

Context & Motivation

When an AI product harms someone, there are three things we should be able to expect. There should be somewhere to report what happened. There should be a clear way to see whether anyone is fixing it. And when the problem is serious, the fix should address the underlying failure and not just patch one product while the same harm remains elsewhere.

Today, those pieces are fragmented. A person can report an incident to a public database, but no provider is required to act on it (REF A). Frontier AI companies have an industry forum for sharing information about serious threats, but it is limited to its members and is not a public reporting or resolution system (REF B). Laws can require providers to report certain incidents to regulators, but those reports are tied to particular jurisdictions and regulatory obligations (REF C).

These mechanisms are useful, but they leave a basic gap: there is no shared system that turns an AI harm reported anywhere into a problem that every participating frontier provider is expected to investigate, resolve, and prevent from recurring. Each provider largely manages the same emerging risks on its own.

There is a precedent for solving a similar coordination problem. In 2006, five major payment card brands created the Payment Card Industry Security Standards Council and shared responsibility for developing and maintaining a common security standard, while continuing to run their own compliance programmes (REF D). Competitors can agree on a shared baseline for a risk that none of them can manage effectively alone. AI needs something similar.

Target Audience

Frontier AI providers that face many of the same safety, security, and societal risks but currently address them independently.

Businesses that want a recognizable standard rather than having to interpret a different set of expectations for every provider that also varies by jurisdiction.

Countries and their representatives that want a meaningful voice in decisions about how frontier AI systems should behave.

People who are harmed by AI and want one place to report what happened, know that it will be fixed.

Customer Problems

Imagine a parent reporting dangerous advice given by one AI assistant. Weeks later, another assistant gives essentially the same advice. The parent has to start over because the two providers do not share a common system for recognizing, investigating, or resolving the problem.

The same problem appears at a societal level. Ask three frontier AI systems about a disputed border, a controversial historical event, or another question where the facts and values are contested, and they may produce three different answers. There may be good reasons for disagreement but today there is no shared institution responsible for making that disagreement visible, deciding when a common standard is needed, or recording that no consensus exists. Do we at least know where we have agreed to disagree?

The underlying problem is not simply that AI systems make mistakes. It is that there is no shared mechanism for turning individual failures into industry-wide learning and shared decisions.

Hypothesis

1. No front door: There is no single place where anyone can report an AI harm and follow the case through investigation and resolution.
2. Isolated fixes: A failure addressed by one provider can remain unresolved in others, allowing the same harm to recur.
3. No shared standard of care: There is no broadly accepted, continuously updated test for what responsible behaviour should look like across frontier AI providers.
4. Slow oversight: Traditional audits and regulation can struggle to keep pace with systems that change weekly or even daily.

Note: For simplicity, this framework focuses on commercially operated frontier AI providers. We do not address open-source models here because their development, distribution, and incentive structures differ substantially and may require a different governance mechanism.

Solutions

1. One front door: Anyone can report an AI harm through one shared service, online or by phone. The reporter receives a case and can see whether it has been accepted, investigated, disputed, or resolved. The system is a reporting and accountability layer, not a promise that every report will result in a particular remedy.
2. One shared harm record: The system builds on existing incident databases rather than replacing them. A validated report becomes a shared record that participating providers can investigate against their own systems. Sensitive information and dangerous findings can remain private where disclosure would create additional risk.
3. A daily governance body: Representatives from participating countries, civil society, technical disciplines, and other relevant fields meet continuously to decide which behaviours or risks should be permitted, prohibited, or left undecided. Decisions apply to every participating provider. Votes record both agreement and disagreement, so the public can see not only the decision but where meaningful disagreement remains.
4. A formal place for disagreement: Not every question will have a defensible consensus. When it does not, the council records the major perspectives, shows where each member stands, and makes the disagreement visible rather than forcing an artificial answer. Where appropriate, communities could also choose to route certain use cases to HomeGrown models built and governed by the communities they serve.
5. A floor, not a ceiling: The shared standard sits beneath local law. Participating providers can adopt stricter requirements or undergo additional assessments, just as individual card brands maintain their own programmes on top of the PCI framework.
6. Shared funding and responsibility: Participating providers fund the council and accept its obligations as part of membership. Businesses using participating models therefore gain access to a common assurance framework rather than having to build an entirely separate governance system for every provider.
7. Live assurance: Assessment does not happen once a year and then disappear until the next audit. Council-funded assessors continuously review available evidence and re-evaluate important controls after significant model or product updates. Providers that repeatedly fail to address validated findings can lose good standing publicly.

Outcome

1. People have somewhere to go. Someone harmed by an AI system no longer has to figure out which company, regulator, database, or lawyer to contact first.
2. Reports become closed loops. A serious report can move from submission to investigation to a documented plan for resolution, rather than disappearing into an inbox or database. Once people have made a plan for an unresolved problem, there is evidence that they may be able to stop carrying the problem cognitively themselves (REF E).
3. The world can transparently agree to disagree. Countries, experts, civil society, and providers gain a standing forum for shaping the rules of frontier AI. Disagreement becomes something that can be recorded and understood rather than hidden behind inconsistent model behaviour.
4. Providers compete above a common floor. Companies can still differentiate on capability, product design, and their own additional standards, while sharing responsibility for a baseline of risks that are too important to leave entirely to individual providers.
5. AI governance becomes an evolving institution rather than a yearly checklist. The standard can change as evidence changes, incidents accumulate, and new capabilities emerge—while preserving a public record of what was decided, why it was decided, and who disagreed.

Appendix

1. The AI Incident Database is a public database of AI incidents and near harms. Anyone can submit incident reports, which are reviewed and indexed. It is intended to help the AI community collectively record and learn from failures.
2. The Frontier Model Forum is an industry-supported forum whose member firms signed a voluntary agreement in March 2025 to share information about frontier AI vulnerabilities, threats, and capabilities of concern. The information-sharing mechanism is currently restricted to member firms and is focused on serious safety and security risks.
3. Regulation (EU) 2024/1689, Article 73 requires providers of high-risk AI systems placed on the EU market to report serious incidents to the relevant market surveillance authorities. Providers must also investigate reported incidents and take corrective action.
4. The PCI Security Standards Council was founded in 2006 by American Express, Discover, JCB International, Mastercard, and Visa. The five founding members share ownership and governance of the Council and each incorporates the PCI Data Security Standard into its own compliance programme. The Council develops and maintains the common standard, while the individual payment brands retain their own compliance programmes and enforcement.
5. Masicampo, E. J. & Baumeister, R. F. (2011). Consider it done! Plan making can eliminate the cognitive effects of unfulfilled goals. Journal of Personality and Social Psychology, 101(4), 667–683. The experiments found that making specific plans for unfulfilled goals reduced intrusive thoughts and other cognitive effects associated with those goals. The study tested plans made by individuals for their own goals; applying this finding to a system that gives people a documented plan for resolving an AI harm is our extension. Read the study