The Real Cost of Bad Data When AI Makes the Decisions
By Jora Gill · · Updated · 5 min read
When bad data fed a spreadsheet, a person usually noticed the odd number before acting on it. When bad data feeds an AI system, the output arrives fluent and confident, and is often acted on with nobody checking.
Where the cost lands
- Customers: wrong advice, refused claims or unfair treatment.
- Legal: liability for what the AI said, as Air Canada found, and discrimination claims where outcomes differ by group.
- Regulatory: data protection and sector regulators expect accuracy and fairness to be evidenced.
- Reputation: AI failures are public, memorable and widely shared.
Why it is hard to see
AI errors rarely look like errors. A biased decision looks like any other decision. An invented policy reads like a real one. Without deliberate testing, problems surface through complaints, journalists or regulators.
Finding it early
Test the system with cases where you know the right answer. Compare outcomes for requests that differ only by a personal characteristic. Check the data represents the people the system serves. Then keep the tests and re-run them, because data changes even when the system does not.
Find out what your AI will get wrong, before anyone else does.
FireBreak independently tests AI for security, reliability, fairness and governance. We never assess systems we built, and we never sell fixes for what we find.