What it means
Machine learning is a way of building AI in which a system learns patterns from examples instead of following rules written by hand. The system is trained on data and then uses what it learned to make predictions or classifications about new cases.
Real-world example
A public library trains a model on five years of loan records to predict demand for new titles at each branch. Instead of librarians writing rules ("crime novels are popular in Branch A"), the model learns patterns from the data, and staff use its forecasts as one input when ordering copies.
Why it matters for AI adoption
Machine learning is only as good as the data it learns from. If historical data is incomplete, outdated or reflects past unfairness, the model will repeat those patterns. Organisations need to understand where training data comes from, how performance is measured, and how the model will be maintained as conditions change.
Questions to ask
- What data was the model trained on, and how well does it represent our real cases?
- How do we measure its performance, and how often will it be checked and retrained?