Interpretowalno

Interpretowalno is a term used in Polish-language discussions of artificial intelligence and related fields to describe the degree to which a system’s decisions, reasoning process, and outputs can be understood by humans. It is often presented as part of the broader concept of interpretability, focusing on how readily explanations and rationales behind model behavior can be grasped, communicated, and evaluated by users and stakeholders.

In practice, interpretowalno encompasses several dimensions. These include the transparency of the model’s structure and data pipelines, the availability of meaningful explanations for predictions, and the use of representations and interfaces that make behavior predictable and consistent. It also involves the alignment of explanations with human reasoning and domain knowledge, so that users can assess credibility and relevance.

Approaches to enhancing interpretowalno vary. They range from employing inherently interpretable models, such as rule-based systems or simple architectures, to applying post-hoc explanation techniques that provide feature attributions or local approximations. Simplifying features, using intuitive visualizations, and offering natural language summaries are common strategies to improve interpretowalno.

Assessment methods for interpretowalno combine objective metrics—fidelity, stability, and simplicity—with human-centered evaluations, such as user studies that measure understanding, trust, and decision quality. Critics warn against overinterpreting explanations, noting that high interpretowalno does not automatically ensure safety, fairness, or robustness.

The term finds use across AI research, ethics, and policy discussions as a way to frame the goal of making intelligent systems more understandable and accountable. See also interpretability, explainable AI, transparency, and accountability.