Selgitatavus

Selgitatavus, formed from Estonian selgitama “to explain” plus the suffix -atavus, refers to the property of a model, process, or analysis to produce explanations that are understandable to humans. In data science and artificial intelligence, selgitatavus describes how clearly the reasoning behind a prediction, decision, or recommendation can be communicated to users, domain experts, or regulators. It is related to, but distinct from, transparency, with selgitatavus emphasizing usefulness and intelligibility for a given audience.

Approaches to selgitatavus fall into two broad categories: inherently interpretable models and post-hoc explanations. Interpretable models include linear models and decision trees with transparent rules. Post-hoc methods provide explanations for opaque models, using techniques such as feature attributions (SHAP, LIME), surrogate models, example-based explanations, or counterfactuals. Explanations can be global, describing the model as a whole, or local, focusing on a specific decision.

Applications span healthcare, finance, public administration, and other sectors where decisions affect people. Selgitatavus is central to debates about algorithmic accountability, user trust, and regulatory expectations. Challenges include balancing accuracy and explainability, tailoring explanations to diverse audiences, avoiding misleading simplifications, and protecting data privacy.

Overall, selgitatavus is an active area of research and practice aimed at making AI and data-driven decisions more transparent and trustworthy.