Interpretowalnoci

Interpretowalnoci is a concept used in information science and artificial intelligence governance to describe the degree to which a system's behavior and internal processes can be understood by humans. In practice, it covers how readily a user or stakeholder can grasp why a model makes a given decision, how transparent the model’s representations are, and how accessible the accompanying documentation and explanations are. The term is closely related to, but distinct from, interpretability and explainability; it is sometimes used in Polish-language writing as an informal variant of interpretowalność (interpretability).

Assessment and methods: Researchers and practitioners aim to increase interpretowalnoci through model simplification, transparent architectures, and explanation techniques. Common approaches include feature attribution (SHAP, LIME), surrogate or transparent models, rule-based approximations, contrastive and counterfactual explanations, and model cards or documentation. User studies and risk assessments are also used to gauge how well different audiences understand the model.

Domains and challenges: Interpretowalnoci is applied in healthcare, finance, law, and public policy to support governance, accountability, and user trust. Challenges include trade-offs between accuracy and interpretability, subjectivity in what counts as understandable, potential for explanations to be misleading, and the risk of privacy or security concerns when exposing internal logic.

See also: Interpretability, explainable AI, model transparency, responsible AI.