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Interpretable Instance-Based Text Classification for Social Science Research Projects

Löfström, Helena; Löfström, Tuwe; Johansson, Ulf


In this study, two groups of respondents have evaluated explanations generated from an instance-based explanation method called WITE (Weighted Instance-based Text Explanations). One group consisted of 24 non-experts who answered a web survey about the words characterising the concepts of the classes and the other group consisted of three senior researchers and three respondents from a media house in Sweden who answered a questionnaire with open questions. The data used originates from one of the researchers’ project on media consumption in Sweden. The results from the non-experts indicate that WITE identified many words that corresponded to the human understanding but also included some insignificant or contrary words as important. In the results from the expert evaluation, there were indications that there is a risk that the explanations could persuade the users of the correctness of a prediction, even if it is incorrect. Consequently, the study indicates that an explanation method could be seen as a new actor which is able to persuade and interact with the humans and cause a change in the results of the classification of a text.

Verlagsausgabe §
DOI: 10.5445/KSP/1000087327/15
Veröffentlicht am 02.03.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Wirtschaftswissenschaften – Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000117500
Erschienen in Archives of Data Science, Series A (Online First)
Band 5
Heft 1
Seiten A15, 27 S. online
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