Reliability
how you make sure ai delivers reliable, accurate output
AI output can look confident and still be wrong. This card is about how the newsroom checks that what AI produces is accurate enough to use, and where errors could slip through. A team pauses here because in journalism a wrong fact carries a real cost.
Questions to explore
- How do you check that AI output is accurate before it reaches your audience?
- Where in your workflow is an AI mistake most likely to go unnoticed?
- What level of accuracy is good enough for each way you use AI?
- How do you tell the difference between output that sounds right and output that is right?
- Who is responsible for catching errors, and do they have time to do it?
Expert voices
“AI technology is not trustworthy by default, and LLMs can make things up. How do we check the accuracy of AI systems and verify their results?”
“AI systems confidently produce false information: fabricated quotes, invented sources, non-existent studies. Journalists must understand why this happens to avoid publishing AI-generated misinformation.”
“The machine is guessing. It is a formulation machine, not an analysis or fact machine, and testing its reliability is difficult and needs to happen over time.”
“AI produces statistically convincing lies that mix real and fake information. It shows you what looks right statistically, not what is true, so treat every AI-generated fact or quote as unverified until checked against a primary source.”
Things to consider
- AI can state something false with the same confidence as something true.
- The risk of an error depends on what the output is used for.
- A check that is skipped under deadline pressure is not a reliable check.
Pull Reliability when it is relevant and set it aside when it is not. Pair it with the other AI Conversations cards, lay them out on a table, and use the questions above to get everyone on the same page. Capture what you discuss on sticky notes or in a shared doc.
More AI Conversations cards
