论文标题
用知识片段丰富人工智能解释
Enriching Artificial Intelligence Explanations with Knowledge Fragments
论文作者
论文摘要
人工智能模型越来越多地用于制造业，以告知决策。负责任的决策需要准确的预测和对模型行为的理解。此外，对模型原理的见解可以充满领域知识。这项研究构建了考虑特定预测的功能排名的解释，并用媒体新闻条目，数据集的元数据和Google知识图中的条目丰富了这些排名。我们将两种方法（基于嵌入的语义和语义基于语义的方法）在需求预测上进行了比较。
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.
