Model Explainability refers to methods that help people understand how and why a machine learning model produces a particular prediction or decision. It reveals which input factors have the greatest influence on an outcome, making complex models easier to interpret. Techniques may examine individual predictions or provide an overall view of how the model behaves across different inputs.
For example, a loan assessment system can show which factors contributed to an applicant’s predicted risk level. This helps organizations identify unexpected patterns, improve trust, investigate errors, and support responsible AI use. It is valuable in finance, healthcare, manufacturing, security, and other fields where understanding automated decisions is important.
