As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
We present a novel counterfactual-based dashboard for explainable artificial intelligence (XAI) in process industries, aimed at enhancing the understanding and adoption of machine learning (ML) models by providing transparency, explainability, and performance evaluation. Our dashboard comprises two modules: a statistical analysis module for data visualization and model performance assessment, and an XAI module for exploring counterfactual explanations at varying levels of abstraction. Through a case study of an industrial batch process, we demonstrate the dashboard’s applicability and potential to increase trust in ML models among stakeholders, paving the way for confident deployment in process industries.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.