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in collaboration with Microsoft

The ten principles

Principle 10 · Before you share · Ownership

Accountability for AI-assisted results

The people and organizations that use AI answer for the results, and problems are reported early.

Key question

Am I ready to put my name to it?

Benefit

A reputation that survives mistakes, because they’re caught early and corrected openly.

Context

AI can draft, recommend and act. Responsibility stays where it was. Whatever goes out under your name, or your company’s, belongs to you and your organization. In 2024 a Canadian tribunal rejected an airline’s argument that its website chatbot was responsible for its own answers: the chatbot was part of the airline’s website, and the airline answered for everything on it.

Owning the result also means owning the mistakes. People who handle AI well say so early when something goes wrong, fix it and help others avoid it.

Handling errors

  1. Stop the work if it’s still running.
  2. Tell the person responsible, and your contact for AI problems if you have one.
  3. Keep what’s needed to understand what happened: the prompt, the sources and the output.
  4. Fix the result and tell the people affected.
  5. Share the lesson, including near misses.

Record-keeping

For work that matters, keep enough to explain how you got the result: what you asked, which sources you used and what you changed. It takes a minute, and it turns “the AI said so” into an answer you can defend. At organizational scale, the OECD AI Principles call for traceable datasets, processes and decisions.

Practices

  1. 01Read everything you send, sign or ship as if it will be quoted back to you.
  2. 02When something goes wrong, say so early, fix it and tell the people affected.
  3. 03Report near misses too, such as the agent that almost sent the wrong file.
  4. 04Keep enough of your work (prompt, sources, output) to explain how you got the result.
  5. 05Share what works with your colleagues.

Prompt examples

A prompt before and after improvement, and the part a person does outside the prompt.

Writing

Finishing a client report

Before

The final report, sent as it came out:

“Write the final client report.”

After

“Draft sections 2 and 3 from my notes and the attached data. Mark every sentence that goes beyond my notes with [CHECK].”

Your part

Clear every [CHECK] yourself, then read the whole report once more from start to finish.

Why it works: Marking what goes beyond your notes shows exactly where your checking is needed.

Writing

Correcting a mistake

Before

The wrong figure fixed in your own copy of the report, and nothing said to the clients.

After

“Draft a short note to the three clients who received last week’s report: the figure on page 4 was wrong (12%, not 21%), here is the corrected page, and nothing else in the report changes.”

Your part

Check the correction yourself, send it today and tell your team what happened so the check gets added.

Why it works: A quick, specific correction builds more trust than a silent fix.

Decisions and plans

Keeping a trail

Before

“Tell me which supplier to choose.”

After

“Compare these three supplier proposals against our criteria (attached) in a table, quoting the proposal for each score. Then list what I should verify before deciding.”

Your part

Verify the listed points, decide, and save the table with your decision and the reason. If anyone asks later, you can show how you chose.

Why it works: Quoting the proposal for each score ties the comparison to evidence, and the list of checks keeps the decision with you.

Evidence

  • A Canadian tribunal held an airline responsible for all the information on its website, including its chatbot’s answers, and found negligent misrepresentation. [Moffatt v. Air Canada]
  • A US federal court fined lawyers and their firm for filing, and then defending, court opinions invented by an AI chatbot. [Mata v. Avianca]
  • A consulting firm repaid part of a government fee after a report with invented references and a made-up court quote was published. [The Register] [CFO Dive]
  • The OECD AI Principles hold AI actors accountable for the proper functioning of AI systems, according to their roles. [OECD]
  • Singapore’s framework for agentic AI keeps the deploying organization and its supervisors accountable for agents’ actions, including autonomous ones. [IMDA]

Case studies and scenarios

Team practice

Name one contact for AI problems, give every shared agent an owner and make near misses easy to report without blame.

Team toolTeam AI charter

Sources

What each source establishes, and its limits. The practices and recommendations on this page are ours, and the facts come from the sources. See every source we use.

  1. Moffatt v. Air Canada, 2024 BCCRT 149 Civil Resolution Tribunal of British Columbia · February 14, 2024 · Tribunal decision Rejected the airline’s argument that its chatbot was responsible for its own answers, held the airline responsible for all the information on its website and found negligent misrepresentation, awarding C$812.02. Limits: A small-claims decision in one province. No evidence was given about how the chatbot worked.
  2. Mata v. Avianca, Inc.: opinion and order on sanctions US District Court, Southern District of New York · June 22, 2023 · Court decision Found that lawyers acted in bad faith by filing, and then defending, court opinions invented by an AI chatbot; fined them and their firm US$5,000 and ordered letters to their client and to the judges falsely named. Limits: One case. Its lessons are about professional duties more than the technology.
  3. Deloitte refunds Aussie gov after AI fabrications slip into $440K welfare report The Register · October 6, 2025 · News report Reports the invented references and made-up court quote in the report for the Department of Employment and Workplace Relations, the corrected version disclosing AI use, and Deloitte’s agreement to repay the final instalment of its fee. Limits: A news report.
  4. Deloitte refunds over $60K for report with AI errors, Australian government says CFO Dive · October 21, 2025 · News report Reports the department’s statement that the refund was more than A$97,000. Limits: A news report of a government statement.
  5. Recommendation of the Council on Artificial Intelligence (OECD AI Principles) OECD · Adopted May 22, 2019; revised May 3, 2024 · Intergovernmental principles Calls for safeguards such as capacity for human agency and oversight (principle 1.2), accountability with traceability of datasets, processes and decisions (1.5), and systems that can be overridden, repaired or shut down safely (1.4). Limits: Not legally binding.
  6. Model AI Governance Framework for Agentic AI Infocomm Media Development Authority, Singapore · Version 1.5, May 20, 2026 (first published January 22, 2026) · Voluntary framework Keeps organizations and their human supervisors accountable for agents’ actions; asks for human approval at significant checkpoints such as deleting data, sending messages and payments; treats very low override rates and very fast reviews as possible signs of rubber-stamping; prefers approvals enforced by system controls over prompts. Limits: Voluntary guidance, not law. Its case studies were supplied by the companies themselves.

Cite this page

Founderz (2026). Principle 10: Accountability for AI-assisted results. The RUAI Standard, 2026 edition. Developed by Founderz in collaboration with Microsoft. https://responsibleai.founderz.com/toolkit/principles/accountability

Licensed under CC BY 4.0: share and adapt with attribution.