GoldenMend

Context & Motivation

When AI gets something wrong and costs someone money, time, or both, they want someone to put it right.

Today, the path is usually fragmented. A consumer AI provider may ask the person to try informal resolution first, then send the dispute to individual arbitration under the provider's terms, with class actions waived and liability capped. For example, a leading frontier lab's current US consumer terms require informal resolution before arbitration, provide for individual arbitration, waive class actions, and cap liability at the greater of fees paid in the previous 12 months or $100, subject to applicable law. That can provide a route for one dispute. It does not create a shared standard for similar disputes, and its outcome does not automatically help the next person.

AI agents make the problem harder. In a 2026 survey of 100 senior finance, payments, and risk leaders at UK online retailers, 58% said AI-initiated transactions had already reached their platforms. When asked about a hypothetical disputed £2,000 AI-agent purchase, respondents split over who should be responsible, and only 41% said they felt very confident in current liability frameworks.

Some routes already exist, and each solves only part of the problem. A British Columbia tribunal held an airline responsible for inaccurate information given by its website chatbot, but the decision was one case in one province. The UK Financial Ombudsman Service is free to consumers and can make decisions that become binding on a firm when the consumer accepts them, but it covers financial businesses rather than AI across industries (REF A). Under the EU Digital Services Act, certified out-of-court bodies can settle disputes between users and platforms, but their decisions are not binding and the service must be free or nominal for users (REF B).

We are only at the beginning of an agentic future. As AI moves from answering questions to booking, buying, cancelling, negotiating, and sending things on our behalf at scale, ordinary disputes could multiply. Many will cross borders. Sending each one to court could be expensive for the person who was harmed and expensive for the companies involved.

SunAccord provides the shared standard and the front door for reporting harms. GoldenMend provides the arbitration and resolution.

Target Audience

People harmed by AI who want a fair way to resolve a dispute without needing a lawyer.

Frontier AI providers that want consistent, predictable rulings instead of scattered cases decided under different laws and in different forums.

Businesses building on AI that need to know where their responsibility ends when something goes wrong between the model, the application, and the customer.

Insurers that want real-world evidence for pricing AI-related risk.

Courts and regulators that want everyday AI disputes resolved before they become expensive legal cases, while retaining the difficult questions for themselves.

Customer Problems

An AI agent books a nonrefundable flight on the wrong date. The travel site blames the model. The AI provider blames the site. The customer has the receipt, but the relevant conversation may be gone, the model may have changed, and neither company agrees that the loss is theirs.

Now imagine the same problem appearing in five countries. At the provider, a legal lead sees similar claims arriving under different laws, through different courts, with no common record of what happened and no consistent answer about who should pay.

The problem is not simply that AI can make mistakes. It is that there is no independent, affordable place designed to answer the everyday question that follows: when AI causes a measurable loss, who should put it right?

Hypothesis

1. Judge of its own case: The provider that may have caused the harm often controls the first complaint process and its terms.
2. Unclear responsibility: When an AI provider and a business built on its model are both involved, each can point to the other.
3. Cost out of proportion: Small harms, especially across borders, can cost more to pursue than they are worth.
4. Lost evidence: Logs disappear, models change, and the evidence needed to reconstruct an AI interaction may no longer exist.
5. Inconsistent outcomes: Similar claims can produce different answers depending on the country, court, contract, or industry involved.
6. Private outcomes: A person may receive compensation without the underlying failure being recognized or corrected for everyone else affected by it.

Solutions

1. Independent panels: GoldenMend assigns cases to rotating panels combining legal, technical, and consumer expertise. The tribunal is funded through a multiyear trust that participating members pay into in advance, with each member paying for the adjudication and resolution for their share of cases through a common framework.
2. One case for everyone involved: The AI provider and the business using its model can join the same case. The panel determines which party made which commitment, where each fell short, and how responsibility should be divided.
3. Free and easy to start: A case can begin through a standardized export function in a participating provider's or business's application. The export packages the relevant conversation, model version, and other available evidence. Community organizations can file on someone's behalf, and free advisers can help claimants understand the process.
4. A clear test: The panel asks whether the member followed the shared standard and its own published promises, whether its actions contributed to the harm, and whether the resulting loss can be established. A disclaimer is less persuasive when an AI acted rather than merely advised, or repeated a failure the member already knew about.
5. Case flow: The member first receives a short opportunity to put things right. Cases are then assessed by severity, and materially similar claims can be grouped around a lead case. A minimum harm threshold keeps the tribunal focused on losses that are meaningful enough to justify adjudication.
6. People decide: AI can sort evidence, translate documents, identify similar cases, and summarize arguments. Claimants can correct the summaries. Only people decide the outcome.
7. A second look: A claimant who believes the process went wrong can request review by a different panel on defined grounds. An accepted decision binds the participating member, while claimants retain any rights they have under applicable law to pursue the matter in court.
8. Rules and rulings stay separate: SunAccord writes the shared standard. GoldenMend applies that standard to individual disputes. When cases expose a gap or recurring failure in the standard, those findings go back to SunAccord for consideration.
9. Fix the individual and learn from the pattern: Remedies are capped and designed to make the claimant whole rather than punish the member. Decisions are published without identifying the people involved. When a ruling identifies a systemic failure affecting others, the relevant fix can be applied more broadly.
10. Courts can focus on what is truly important. Ordinary disputes can be resolved through a faster, specialized process, while novel legal questions, serious harms, and cases outside the tribunal's scope remain available to courts and regulators.

Note: GoldenMend does not cover criminal matters, intellectual property disputes, catastrophic risks, or open-source models. Each raises different questions of responsibility and requires its own route.

Outcome

1. People can get AI-related losses put right without hiring a lawyer. A person does not have to become an expert in AI, contract law, or jurisdiction before asking for help.
2. A body of decisions shows what reasonable care looks like. Over time, published rulings create a practical record of how the shared standard applies to real situations.
3. Providers trade scattered disputes for a predictable process. Instead of defending similar claims independently across countries and forums, participating providers know where disputes go, how they will be assessed, and what the limits are.
4. Businesses know where responsibility sits. Companies building on AI can make clearer decisions about which risks belong with the model provider, which belong with the application, and which must be shared.
5. Insurers get evidence of actual AI risk. A consistent record of claims and outcomes gives insurers something better than hypothetical scenarios from which to price emerging risks.

Appendix

1. Read how decisions work
2. Read the regulation