グローバル解釈は、AIの 人工知能（AI）の分野において (AI) that focuses on understanding the behavior and decision-making processes of AIモデル at a holistic level. Unlike local interpretation, which examines specific predictions or outputs for individual instances, global interpretation seeks to provide insights into how an AI model functions across a wide range of inputs and scenarios.
このアプローチは、AIシステムの透明性と説明責任を確保するために不可欠です。 AIシステム, particularly in high-stakes applications such as healthcare, finance, and 法執行. By examining the model as a whole, stakeholders can identify patterns, biases, and correlations that may not be evident when looking at isolated predictions. Techniques used for global interpretation include feature importance analysis, partial dependence plots, and model-agnostic methods like LIME (ローカル解釈可能モデル非依存の説明）やSHAP（SHapley Additive exPlanations）など。
Global interpretation not only aids developers and researchers in refining AI models but also supports 規制遵守 and ethical standards by promoting understanding and trust among users and affected communities. It emphasizes the importance of interpretability in AI, enabling stakeholders to make informed decisions based on how an AI model behaves under various conditions.