Learn AI / Learning Paths /How to assess an AI proposal
How to assess an AI proposal
The ten questions to ask a vendor, a colleague, or a concept note, in the order you should ask them.
Evaluation
Evaluation checks how well a system performs on a defined task, while metrics are the specific measures and evals are the tests used to produce them.
Baseline
A reference method or result used to judge how a model performs against a relevant starting point.
Accuracy
The share of cases a system labels correctly out of all the cases it was tested on.
False positives and false negatives
False positives are wrong yes predictions, while false negatives are wrong no predictions.
Data governance
Data governance is the set of rules, roles and checks that control how data is collected, stored, used, shared and corrected over time.
Impact assessment
A structured review used before or during AI use to identify who could be affected, what harms or rights risks may arise, and what controls or human decisions are needed.
Human oversight
Human oversight is the processes that let people monitor AI use, review its outputs and effects, and intervene when needed.
Deployment
Deployment means putting an AI system into real use so people or software can send it live inputs and receive live outputs.
Cost per token
The price charged for each small piece of text a model processes or generates, usually counted separately for input and output.
AI audit and assurance
A structured check of how an AI system is governed, tested and used, producing evidence-based confidence that stated requirements are met and risks are controlled.