Global Interpretability

Global interpretability in machine learning aims to understand the overall decision-making process of complex models, moving beyond local explanations of individual predictions. Current research focuses on developing methods that provide globally consistent and human-understandable explanations, often employing techniques like spectral analysis, Boolean formulas, and rule extraction from neural networks, as well as exploring the computational complexity of achieving global interpretability across different model types. This pursuit is crucial for building trust in AI systems, ensuring fairness and accountability, and facilitating the adoption of machine learning in high-stakes domains like healthcare and finance.