Kolding, Lucas Joachim

Pedersen, Emil Sejer

Povlsen, Marie Margrethe Riskær

Westergaard, Frederikke Rønne

Work, Andreas

This paper seeks to investigate the field of artificial intelligence, more specifically, the explainability of machine learning models. The problem we attempt to solve is how to create an object-oriented infrastructure, and how to achieve interpretability through dashboard technology. Since machine learning is a growing field within artificial intelligence, we found it interesting to explore how to understand and convey machine learn- ing models. Methodologies used in this project to explore our problem includes CRISP-DM, Data pre-processing, UML, LIME, object-oriented programming and regressions. The analysis explores our code using the TRIN-model. Furthermore, the object-oriented approach is analyzed and lastly the explainability gained from the dashboard technology is exam- ined. We can conclude that our solution did increase the explainability of the machine learning models. However, as our development ceased in the CRISP-DM modelling phase, we did not reach a point where we could evaluate whether or not our solution reached a sufficient level of explain- ability. Furthermore, we conclude that the object-oriented approach has its applications in certain situations, but like any methodology, it should be chosen carefully

Explainable Artificial Intelligence: How to increase interpretability through object-oriented programming and dashboard technology

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