Six Data Problems That Make AI Unreliable
By Jora Gill · · Updated · 6 min read
When an AI system gives a wrong answer, the model is often blamed. Frequently the cause is the data it learned from or the documents it retrieves. Here are six data problems that make AI unreliable.
1. Out-of-date sources
An assistant reading last year's policy will confidently give last year's answer.
2. Contradictory documents
When two sources disagree, the AI may pick either, so the same question gets different answers.
3. Gaps
Where no approved source covers a question, many systems fill the gap by inventing an answer.
4. Unrepresentative data
If the data mostly reflects one group of people, the system works less well for everyone else. That is a reliability problem and a fairness problem.
5. Duplicates and errors
Repeated or mislabelled records skew what a model learns and what a search retrieves.
6. Drift
The world changes after the data was collected. Products, prices, rules and customer behaviour move on, and accuracy quietly falls.
How to test
Ask the AI questions whose correct answers you know, including questions that touch updated, conflicting and missing sources. Check whether answers cite approved documents, stay consistent across repeats, and admit uncertainty. Compare accuracy across different groups of users. Repeat the tests on a schedule so drift is caught early.
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.