IBM announce Uncertainty Quantification 360 toolkit

IBM has created the Uncertainty Quantification 360 toolkit and made it available to the open-source community. Released at the 2021 IBM Data & AI Digital Developer Conference, Uncertainty Quantification 360 (UQ360) is aimed at giving data scientists and developers algorithms to streamline quantifying, evaluating, improving and communicating uncertainty of machine learning models.

The UQ toolkit is designed to boost the safety of AI models by giving them the “intellectual humility” they need to use when they’re unsure of something. It’s a collection of algorithms that can be used to quantify an AI model’s uncertainty. It also provides capabilities to measure and improve uncertainty quantification to streamline development processes, as well as taxonomy and guidance to help developers choose which capabilities are appropriate for specific models.

IBM research staff members Prasanna Sattigeri and Q. Vera Liao explained in a blog post, that the choice of UQ method depends on a number of factors, including the underlying model, the type of machine learning task, characteristics of the data, and the user’s goal. Sometimes a chosen UQ method might not produce high-quality uncertainty estimates and could mislead users, so it’s crucial for developers to evaluate the quality of UQ and improve the quantification quality if necessary before deploying an AI system.

UQ360 can improve different kinds of AI

Sattigeri and Liao said UQ 360 can be used to improve hundreds of different kinds of AI models where safety is a paramount concern, an example they provide is AI that’s used to diagnose medical issues such as sepsis.

“Early detection of sepsis is important and AI can help, but only when predictions are accompanied by meaningful uncertainty estimates,” Sattigeri and Liao explained. “Only then can doctors immediately treat patients AI has confidently flagged as at-risk and prescribe additional diagnostics for those AI has expressed a low level of certainty about. If the model produces unreliable uncertainty estimates, patients may die.”

The researchers suggest that knowing the margin of error might also be useful for real estate agents who use AI-based house price prediction models, or for models that try to predict the impact of new product features.

“For every UQ algorithm provided in the UQ360 Python package, a user can make a choice of an appropriate style of communication by following our psychology-based guidance on communicating UQ estimates, from concise descriptions to detailed visualisations,” the researchers explained.

The UQ 360 toolkit is available to download now, and IBM is asking the community to contribute to its development going forward to ensure that AI practitioners can understand and communicate the limitations of their algorithms.