Enriching Artificial Intelligence Explanations with Knowledge Fragments

Jože Rožanec, Elena Trajkova, Inna Novalija, Patrik Zajec, Klemen Kenda, Blaž Fortuna and Dunja Mladenić
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Jože Rožanec: Jožef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia
Elena Trajkova: Faculty of Electrical Engineering, University of Ljubljana, Tržaška c. 25, 1000 Ljubljana, Slovenia
Inna Novalija: Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia
Patrik Zajec: Jožef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia
Klemen Kenda: Jožef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia
Blaž Fortuna: Qlector d.o.o., Rovšnikova 7, 1000 Ljubljana, Slovenia
Dunja Mladenić: Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia

Future Internet, 2022, vol. 14, issue 5, 1-13

Abstract: 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 the 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. The embeddings-based approach measures the similarity between relevant concepts and retrieved media news entries and datasets’ metadata based on the word movers’ distance between embeddings. The semantic-based approach recourses to wikification and measures the Jaccard distance instead. The semantic-based approach leads to more diverse entries when displaying media events and more precise and diverse results regarding recommended datasets. We conclude that the explanations provided can be further improved with information regarding the purpose of potential actions that can be taken to influence demand and to provide “what-if” analysis capabilities.

Keywords: explainable artificial intelligence; human-centric artificial intelligence; smart manufacturing; demand forecasting; Industry 4.0; Industry 5.0 (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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