Inside IBM’s Open-Source Toolkit To Measure Uncertainty In ML Models

It’s essential to understand the inherent uncertainties machine learning models carry to ensure fairness, build trust, and improve decision-making. Despite being an important factor, uncertainty is often overlooked in the context of machine learning-assisted decision making. To this end, IBM released the Uncertainty Quantification 360 (UQ360) open-source toolkit to provide developers and data scientists with a guideline/process to quantify, evaluate, improve, and communicate the uncertainty of machine learning models. The AI toolkit was introduced at the recent IBM Data & AI Digital Developer Conference. With a guideline in place, as in the case of UQ360, developers will be able to estimate the uncertainty in ML model prediction and evaluate them and, if needed, improve their q