Convincing arguments are not always right
One false premise, a correlation taken for a cause, a rough calculation, and the conclusion ends up wrong.
- 57%
- of professionals have made mistakes in their work because of AI
- 58%
- of professionals rely on AI without properly evaluating its output
Definition
What is a reasoning error?
An AI answer can be well structured and still rest on a false premise, a rough calculation or a hasty generalisation.
The risk is all the greater when the reasoning seems coherent: each step appears to follow from the last, concealing a fundamental error.
What these risks cost the business
Legal risk
An analysis built on a false premise can lead to wrong advice, calculations or commitments, with contractual or regulatory consequences.
Reputational risk
A wrong conclusion presented with confidence, internally or externally, calls the company's credibility into question.
Operational risk
Decisions based on rough calculations or hasty generalisations waste time, budget and opportunities.
Measure
How Taboo assesses reasoning errors
Our AI assistant offers conclusions that seem logical but rest on errors. Taboo checks whether employees retrace the steps before acting on them.
- Checking the consistency of information
- Inspecting each reasoning step
- Assessing the relevance of each source
Can you summarise the study for Claire?
9 out of 10 customer requests are handled by AI… 34%
Frequently asked questions
How do you check AI-generated reasoning?
In three steps: understand the structure of the argument, check that each step follows from the last, and assess the relative relevance of the sources.
Do recent models still make mistakes?
Yes. They reason better than before, but remain vulnerable to false premises and calculation errors. Human review is still essential.
Discover and in a live demo
In 30 minutes, we walk you through the test, the report and the training programme, tailored to your organisation.