How to Explain the Prediction of a Machine Learning Model?
Lilian Weng
Machine learning models deployed in critical sectors like healthcare, finance, and criminal justice require interpretability so stakeholders can understand and trust their decisions. The article reviews interpretable models such as linear regression, naive Bayes, and decision trees, plus techniques for explaining black-box models including prediction decomposition and local gradient explanation. Increased model interpretability enables organizations to meet regulatory requirements, build user trust, and deploy high-stakes AI systems responsibly in real-world applications.
Why it matters
The machine learning models have started penetrating into critical areas like health care, justice systems, and financial industry. Thus to figure out how the models make the decisions and make sure the decisioning process is aligned with the ethnic requirements or legal regulations becomes a necessity.
