Liem, JohannesHenkin, RafaelWood, JoTurkay, CagatayMadeiras Pereira, João and Raidou, Renata Georgia2019-06-022019-06-022019978-3-03868-088-8https://doi.org/10.2312/eurp.20191141https://diglib.eg.org:443/handle/10.2312/eurp20191141Data-driven stories, widely used in journalism and scientific communication, match well with the recent focus on interpretable machine learning and AI explainability. Current technologies allow authors to break away from narratives that reflect traditional analytical workflows. To support designing such types of stories, we introduce a descriptive framework that helps identifying narrative patterns and other characteristics of algorithm-related stories. We describe the design space within the framework and demonstrate how to apply to an example of an algorithm-centered story, discussing potential future steps.Humancentered computingVisualization theoryconcepts and paradigmsVisualization design and evaluation methods10.2312/eurp.2019114141-43