Utilizing a Transparency-driven Environment toward Trusted Automatic Genre Classification: A Case Study in Journalism History

Bilgin, A., Hollink, L., van Ossenbruggen, J., Tjonk Kim Sang, E., Smeenk, K., Harbers, F. & Broersma, M., 1-Nov-2018, p. 1-11. 12 p.

Research output: Contribution to conferencePaperAcademic

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.
Original languageEnglish
Number of pages12
Publication statusPublished - 1-Nov-2018
EventIEEE eScience Conference 2018 - Amsterdam , Netherlands
Duration: 29-Oct-20181-Nov-2018


ConferenceIEEE eScience Conference 2018
Internet address


IEEE eScience Conference 2018


Amsterdam , Netherlands

Event: Conference


  • Machine Learning, Transparency, Journalism History, Genre, Automatic Content Analysis

ID: 65580237