Work

XAIProcessLens: A Counterfactual-Based Dashboard for Explainable AI in Process Industries

Year: 2023

Type: book-chapter

Abstract: 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.

Source: Frontiers in artificial intelligence and applications

DOI: 10.3233/faia230110

Authors: Gianluca Manca, Nilavra Bhattacharya, Sylvia Maczey, Dawid Ziobro, Emmanuel Brorsson, Magnus Bång

Institutions: ABB (Germany), The University of Texas at Austin, ABB (Sweden), Linköping University

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Bundesministerium für Bildung und Forschung

Award: G7468167099