XAI-KG: knowledge graph to support XAI and decision-making in
  manufacturing
  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.
