Quantitative Explanation

Quantitative explanation in various fields, from machine learning to social sciences, aims to provide numerical measures and interpretations of complex processes and model behaviors, going beyond simple accuracy metrics. Current research focuses on developing methods to explain model decisions (e.g., using Shapley values or analyzing the impact of contextual factors), assessing fairness and bias in models, and applying these techniques to diverse domains including natural language processing, object detection, and autonomous systems. This work is crucial for building trust in AI systems, improving model interpretability, and facilitating more robust and reliable scientific inquiry across disciplines.