Selitysmalli

Selitysmalli is a Finnish term used to describe a model whose primary purpose is to provide understandable explanations for data patterns, decisions, or the behavior of other models. It is used in machine learning, statistics, and data science to enhance transparency, trust, and the ability to justify outcomes to stakeholders.

A selitysmalli can be an intrinsically interpretable model that offers global explanations, or it can be a surrogate or post-hoc explanation for a more complex, opaque model. Types include global explanations that summarize overall system behavior and local explanations that clarify individual predictions. Common approaches involve using an interpretable surrogate model, such as a decision tree or linear model, to approximate a black-box model; rule-based models; feature importance scores; SHAP or LIME-style local explanations; counterfactual explanations; and visualizations like partial dependence plots.

In practice, the design of a selitysmalli balances interpretability, fidelity to the original model, and usefulness for stakeholders. It is often evaluated in terms of explainability, actionability, and trust rather than solely predictive accuracy. Regulatory and ethical considerations influence how selitysmalli are constructed and presented, including the obligation to provide explanations to users or to satisfy transparency requirements in sensitive domains. The concept emphasizes that explanations should be accessible, relevant, and aligned with the audience’s needs.