Utilizing a transparency-driven environment toward trusted automatic genre classification: A case study in journalism history

With the growing abundance of unlabeled data in real-world tasks, researchers have to rely on the predictions given by black-boxed computational models. However, it is an often neglected fact that these models may be scoring high on accuracy for the wrong reasons. In this paper, we present a practical impact analysis of enabling model transparency by various presentation forms. For this purpose, we developed an environment that empowers non-computer scientists to become practicing data scientists in their own research field. We demonstrate the gradually increasing understanding of journalism historians through a real-world use case study on automatic genre classification of newspaper articles. This study is a first step towards trusted usage of machine learning pipelines in a responsible way.

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presentation Towards Transparent Linguistic Analysis of Dutch Newspaper Article Genres using Machine Learning A. Bilgin (Aysenur), E. Tjong Kim Sang (Erik), K. Smeenk (Kim), T. Klaver (Tom), L. Hollink (Laura), J.R. van Ossenbruggen (Jacco), F. Harbers (Frank) and M. Broersma (Marcel)
techReport Utilizing a transparency-driven environment toward trusted automatic genre classification: A case study in journalism history A. Bilgin (Aysenur), L. Hollink (Laura), J.R. van Ossenbruggen (Jacco), E. Tjong Kim Sang (Erik), K. Smeenk (Kim), F. Harbers (Frank) and M. Broersma (Marcel)