Visualising trustworthiness for machine-generated news

Ali Abdel Rehim, 2019 Thesis

News articles generated by artificial intelligence rather than human reporters are referred to as automated journalism. This thesis explores how to create a trustworthy representation of news articles that mainly are generated by algorithmic decisions. The hypothesis of this thesis takes the background (characteristics of the underlying system design) and the foreground (millennials news consumption behaviour) contexts into consideration in order to provide an optimal approach for trustworthy representation of autogenerated articles. A theory about algorithmic transparency in the news media has been investigated to reveal information about the systems selection processes. The principles of glanceability and the heuristic principles are applied to proposed design solutions (interactive features). The outcomes show that newsreaders are positive towards a system that is trying to encourage them to fact-check the articles. Additionally, the outcomes also contributed to the understanding of how newsreaders can consume autogenerated news.

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Workshop
Design workshop exploring page composition

Coloured box sketch
Machine-generated news draws upon various sources. This concept colour-codes regions of the story to indicate sources. In this case, red showing right-leaning, blue left-leaning sources.