Learn AI / Learning Paths /AI for programme and policy officers
AI for programme and policy officers
How a prediction is made, how it is judged, and how it quietly goes wrong once it is in the field.
Data
Recorded observations, measurements or examples that people or systems can store, examine and use for a purpose.
Dataset
A dataset is an organised collection of examples that a system can use for analysis, training, testing or search.
Classification
A machine learning method that assigns one or more predefined category labels to an input based on learned patterns.
Predictive AI
A system that uses past data to estimate what is likely to happen next, such as a future value, category or level of risk.
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.
Distribution shift and data drift
These terms describe mismatches between development and live data, including changed inputs, labels or relationships, which can reduce a system's reliability.
Algorithmic bias
Systematic unfair patterns in data, rules or model outputs can disadvantage some people or groups, especially when tools are used in important decisions.
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.