La interpretación global es un concepto en la campo de la Inteligencia Artificial (AI) that focuses on understanding the behavior and decision-making processes of modelos de IA 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.
Este enfoque es esencial para garantizar la transparencia y la responsabilidad de sistemas de IA, particularly in high-stakes applications such as healthcare, finance, and aplicación de la ley. 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 (Explicaciones Locales Interpretables de Modelos Agnósticos) o SHAP (Explicaciones Aditivas de Shapley).
Global interpretation not only aids developers and researchers in refining AI models but also supports cumplimiento normativo 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.