AI-Assisted and Explainable Hate Speech Detection for Social Media Moderators – A Design Science Approach

dc.contributor.author Bunde, Enrico
dc.date.accessioned 2020-12-24T19:14:17Z
dc.date.available 2020-12-24T19:14:17Z
dc.date.issued 2021-01-05
dc.description.abstract To date, the detection of hate speech is still primarily carried out by humans, yet there is great potential for combining human expertise with automated approaches. However, identified challenges include low levels of agreement between humans and machines due to the algorithms’ missing expertise of, e.g., cultural, and social structures. In this work, a design science approach is used to derive design knowledge and develop an artifact, through which humans are integrated in the process of detecting and evaluating hate speech. For this purpose, explainable artificial intelligence (XAI) is utilized: the artifact will provide explanative information, why the deep learning model predicted whether a text contains hate. Results show that the instantiated design knowledge in form of a dashboard is perceived as valuable and that XAI features increase the perception of the artifact’s usefulness, ease of use, trustworthiness as well as the intention to use it.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2021.154
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70766
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Explainable Artificial Intelligence (XAI)
dc.subject deep learning
dc.subject design science
dc.subject explainable artificial intelligence
dc.subject hate speech detection
dc.title AI-Assisted and Explainable Hate Speech Detection for Social Media Moderators – A Design Science Approach
prism.startingpage 1264
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