Enriching Artificial Intelligence Explanations with Knowledge Fragments
This work addresses the need for more interpretable AI in manufacturing decision-making, but it appears incremental as it builds on existing explanation methods by adding knowledge fragments.
The paper tackled the problem of improving AI model explanations in manufacturing by enriching feature rankings with external knowledge sources like news, metadata, and Google Knowledge Graph, and found that two approaches (embeddings-based and semantic-based) were compared on a real-world demand forecasting use case.
Artificial Intelligence models are increasingly used in manufacturing to inform decision-making. Responsible decision-making requires accurate forecasts and an understanding of the models' behavior. Furthermore, the insights into models' rationale can be enriched with domain knowledge. This research builds explanations considering feature rankings for a particular forecast, enriching them with media news entries, datasets' metadata, and entries from the Google Knowledge Graph. We compare two approaches (embeddings-based and semantic-based) on a real-world use case regarding demand forecasting.