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Enriching Artificial Intelligence Explanations with Knowledge Fragments

Author

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  • Jože Rožanec

    (Jožef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia
    Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia
    Qlector d.o.o., Rovšnikova 7, 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
    Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia)

  • Klemen Kenda

    (Jožef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia
    Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia
    Qlector d.o.o., Rovšnikova 7, 1000 Ljubljana, Slovenia)

  • Blaž Fortuna

    (Qlector d.o.o., Rovšnikova 7, 1000 Ljubljana, Slovenia)

  • Dunja Mladenić

    (Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia)

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.

Suggested Citation

  • Jože Rožanec & Elena Trajkova & Inna Novalija & Patrik Zajec & Klemen Kenda & Blaž Fortuna & Dunja Mladenić, 2022. "Enriching Artificial Intelligence Explanations with Knowledge Fragments," Future Internet, MDPI, vol. 14(5), pages 1-13, April.
  • Handle: RePEc:gam:jftint:v:14:y:2022:i:5:p:134-:d:805163
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    References listed on IDEAS

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    Cited by:

    1. David Mhlanga, 2022. "Human-Centered Artificial Intelligence: The Superlative Approach to Achieve Sustainable Development Goals in the Fourth Industrial Revolution," Sustainability, MDPI, vol. 14(13), pages 1-22, June.

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