Utilizing a Transparency-driven Environment toward Trusted Automatic Genre Classification: A Case Study in Journalism HistoryBilgin, A., Hollink, L., van Ossenbruggen, J., Tjonk Kim Sang, E., Smeenk, K., Harbers, F. & Broersma, M. 2018 (Accepted/In press) 11 p.
Research output: Contribution to conference › Paper
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.
|Number of pages||11|
|State||Accepted/In press - 2018|
|Event||IEEE eScience Conference 2018 - Amsterdam , Netherlands|
Duration: 29-Oct-2018 → 1-Nov-2018
|Conference||IEEE eScience Conference 2018|
|Period||29/10/2018 → 01/11/2018|
IEEE eScience Conference 2018
29/10/2018 → 01/11/2018Amsterdam , Netherlands
- Machine Learning, Transparency, Journalism History, Genre, Automatic Content Analysis