Uncertainty Quantification in Machine Learning: Measuring Confidence in Predictions

In this SEI Podcast, Dr. Eric Heim, a senior machine learning research scientist at Carnegie Mellon University's Software Engineering Institute (SEI), discusses the quantification of uncertainty in machine-learning (ML) systems. ML systems can make wrong predictions and give inaccurate estimates for the uncertainty of their predictions. It can be difficult to predict when their predictions will be wrong. Heim also discusses new techniques to quantify uncertainty, identify causes of uncertainty, and efficiently update ML models to reduce uncertainty in their predictions. The work of Heim and colleagues at the SEI Emerging Technology Center closes the gap between the scientific and mathematical advances from the ML research community and the practitioners who use the systems in real-life contexts, such as software engineers, software developers, data scientists, and system developers.