Reviewing the Need for Explainable Artificial Intelligence (xAI)

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dc.contributor.authorGerlings, Julie
dc.contributor.authorShollo, Arisa
dc.contributor.authorConstantiou, Ioanna
dc.date.accessioned2020-12-24T19:14:32Z
dc.date.available2020-12-24T19:14:32Z
dc.date.issued2021-01-05
dc.description.abstractThe diffusion of artificial intelligence (AI) applications in organizations and society has fueled research on explaining AI decisions. The explainable AI (xAI) field is rapidly expanding with numerous ways of extracting information and visualizing the output of AI technologies (e.g. deep neural networks). Yet, we have a limited understanding of how xAI research addresses the need for explainable AI. We conduct a systematic review of xAI literature on the topic and identify four thematic debates central to how xAI addresses the black-box problem. Based on this critical analysis of the xAI scholarship we synthesize the findings into a future research agenda to further the xAI body of knowledge.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.156
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70768
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectExplainable Artificial Intelligence (XAI)
dc.subjectexplainable ai
dc.subjectmachine learning
dc.subjectsocio-technical
dc.subjectstakeholders
dc.subjectxai
dc.titleReviewing the Need for Explainable Artificial Intelligence (xAI)
prism.startingpage1284

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