explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
This addresses the need for better interpretability and control in machine learning for users across expertise levels, though it is incremental as it builds on existing tools like TensorBoard.
The authors tackled the problem of making machine learning models more understandable and improvable by developing a visual analytics framework that integrates interactive and explainable AI methods, with a user-study showing it leads to an informed process.
We propose a framework for interactive and explainable machine learning that enables users to (1) understand machine learning models; (2) diagnose model limitations using different explainable AI methods; as well as (3) refine and optimize the models. Our framework combines an iterative XAI pipeline with eight global monitoring and steering mechanisms, including quality monitoring, provenance tracking, model comparison, and trust building. To operationalize the framework, we present explAIner, a visual analytics system for interactive and explainable machine learning that instantiates all phases of the suggested pipeline within the commonly used TensorBoard environment. We performed a user-study with nine participants across different expertise levels to examine their perception of our workflow and to collect suggestions to fill the gap between our system and framework. The evaluation confirms that our tightly integrated system leads to an informed machine learning process while disclosing opportunities for further extensions.