Explainability in Machine Learning

Machine learning's growing prominence in various sectors underscores the importance of explainability, as models increasingly make decisions traditionally handled by humans. Explainability involves elucidating how models, which learn from data rather than explicit programming, arrive at their decisions, a task complicated by the opacity of complex techniques like deep learning. This transparency is crucial for accountability, particularly in regulated industries like finance and healthcare, where decisions are subject to scrutiny. The article outlines different explainability techniques, such as local, cohort, and global model explainability, each serving distinct purposes in understanding model behavior, from individual decision analysis to overarching model performance. Seldon, a company with extensive experience in deploying machine learning models, offers solutions aimed at simplifying the integration and management of machine learning systems, allowing businesses to harness AI while maintaining transparency and control.