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dc.contributor.authorSoroko, Daria
dc.contributor.authorNdöge, Nina
dc.contributor.authorAl-Shafeei, Ahmed
dc.contributor.authorHeuer, Hendrik
dc.contributor.editorAlt, Florian
dc.contributor.editorBulling, Andreas
dc.contributor.editorDöring, Tanja
dc.date.accessioned2019-08-22T04:36:46Z
dc.date.available2019-08-22T04:36:46Z
dc.date.issued2019
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/24668
dc.description.abstractThis paper presents a functional prototype for an interactive web-based interface i_sift developed to foreground the decision-making process of an algorithm that detects similarities in legal texts through word embeddings. Using this as a case study in Computational Social Science, our goal is, first, to highlight the importance of making computational tools and methods transparent to social scientists. Secondly, we suggest an approach that accomplishes this using methods and principles from Interactive Machine Learning and the Algorithmic Experience framework.en
dc.description.urihttps://dl.acm.org/authorize?N681357
dc.language.isoen
dc.publisherACM
dc.relation.ispartofMensch und Computer 2019 - Tagungsband
dc.relation.ispartofseriesMensch und Computer
dc.subjectAlgorithmic Experience
dc.subjectAlgorithmic Transparency
dc.subjectComputational Social Science
dc.subjectVisualization
dc.subjectInteractive Machine Learning
dc.titleUnpacking a model: An interactive visualization of a text similarity algorithm for legal documentsen
dc.typeText/Conference Paper
dc.pubPlaceNew York
mci.document.qualitydigidoc
mci.conference.sessiontitleMCI: Interactive Demos
mci.conference.locationHamburg
mci.conference.date8.-11. September 2019
dc.identifier.doi10.1145/3340764.3345371


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