XAI-KG: knowledge graph to support XAI and decision-making in manufacturing
This work addresses the problem of improving trust and decision-making in AI for manufacturing professionals, but it is incremental as it builds on existing XAI techniques with a domain-specific application.
The paper tackles the lack of quality assessment for explainable AI (XAI) explanations and their impact on decision-making in manufacturing by proposing an ontology and knowledge graph to collect feedback on forecasts, explanations, and user actions, validated on real-world demand forecasting data.
The increasing adoption of artificial intelligence requires accurate forecasts and means to understand the reasoning of artificial intelligence models behind such a forecast. Explainable Artificial Intelligence (XAI) aims to provide cues for why a model issued a certain prediction. Such cues are of utmost importance to decision-making since they provide insights on the features that influenced most certain forecasts and let the user decide if the forecast can be trusted. Though many techniques were developed to explain black-box models, little research was done on assessing the quality of those explanations and their influence on decision-making. We propose an ontology and knowledge graph to support collecting feedback regarding forecasts, forecast explanations, recommended decision-making options, and user actions. This way, we provide means to improve forecasting models, explanations, and recommendations of decision-making options. We tailor the knowledge graph for the domain of demand forecasting and validate it on real-world data.