Air Canada chatbot held liable for false refund promise
A Canadian tribunal ordered Air Canada to pay a customer damages after its website chatbot gave incorrect information about bereavement fares, setting a notable legal precedent for AI-generated business communications.
The damage was public. The root cause was preventable.
A Canadian tribunal ordered Air Canada to pay a customer damages after its website chatbot gave incorrect information about bereavement fares, setting a notable legal precedent for AI-generated business communications.
What the software actually got wrong
Air Canada's customer-service chatbot gave inaccurate guidance about the airline's bereavement-fare refund policy, and the airline had no process to ensure the chatbot's answers matched its actual written policy, then argued (unsuccessfully) that it was not responsible for the bot's statements.
Air Canada's customer-service chatbot gave inaccurate guidance about the...
Air Canada's customer-service chatbot gave inaccurate guidance about the airline's bereavement-fare refund policy
Requs AI Edge Case flags this exact pattern at the requirements and architecture stage — before a single line of code implementing it exists — so the assumption behind it gets challenged while it is still cheap to fix.
The airline had no process to ensure the chatbot's...
the airline had no process to ensure the chatbot's answers matched its actual written policy, then argued (unsuccessfully) that it was not responsible for the bot's statements
Requs AI Software FMEA traces this failure mode back to the system-level hazard it feeds, tagging it against the Common Defect Enumeration so it surfaces in review instead of in the field.
Beyond code coverage and "shall" testing
Root causes like this one rarely show up in code coverage or requirements-compliance testing, because nobody wrote a requirement anticipating the specific edge case that broke. Requs AI Edge Case and Requs AI Software FMEA are built to surface exactly this class of overlooked failure mode — before the software is written.
Surfaces this before code exists
Identifies edge cases like this one at requirements and architecture time, using the Common Defect Enumeration to catalog failure patterns seen across hundreds of real-world software failures — including this one.
Connects the failure mode to the hazard
Traces this class of root cause directly to the system-level hazard it can produce, so a defect pattern like this one gets flagged during design review instead of after it ships.
Find the overlooked root causes before they ship.
Schedule a demonstration, or explore how Requs AI Edge Case and Software FMEA use the Common Defect Enumeration.