Improved explanatory efficacy on human affect and workload through interactive process in artificial intelligence

Cited 3 time in webofscience Cited 4 time in scopus
  • Hit : 288
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKim, Byung Hyungko
dc.contributor.authorKoh, Seunghunko
dc.contributor.authorHuh, Sejoonko
dc.contributor.authorJo, Sung-Hoko
dc.contributor.authorChoi, Sungheeko
dc.date.accessioned2020-10-28T01:55:07Z-
dc.date.available2020-10-28T01:55:07Z-
dc.date.created2020-10-21-
dc.date.created2020-10-21-
dc.date.created2020-10-21-
dc.date.created2020-10-21-
dc.date.created2020-10-21-
dc.date.issued2020-10-
dc.identifier.citationIEEE ACCESS, v.8, pp.189013 - 189024-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10203/277007-
dc.description.abstractDespite recent advances in the field of explainable artificial intelligence systems, a concrete quantitative measure for evaluating the usability of such systems is nonexistent. Ensuring the success of an explanatory interface in interacting with users requires a cyclic, symbiotic relationship between human and artificial intelligence. We, therefore, propose explanatory efficacy, a novel metric for evaluating the strength of the cyclic relationship the interface exhibits. Furthermore, in a user study, we evaluated the perceived affect and workload and recorded the EEG signals of our participants as they interacted with our custom-built, iterative explanatory interface to build personalized recommendation systems. We found that systems for perceptually driven iterative tasks with greater explanatory efficacy are characterized by statistically significant hemispheric differences in neural signals with 62.4% accuracy, indicating the feasibility of neural correlates as a measure of explanatory efficacy. These findings are beneficial for researchers who aim to study the circular ecosystem of the human-artificial intelligence partnership.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleImproved explanatory efficacy on human affect and workload through interactive process in artificial intelligence-
dc.typeArticle-
dc.identifier.wosid000586963600001-
dc.identifier.scopusid2-s2.0-85102762907-
dc.type.rimsART-
dc.citation.volume8-
dc.citation.beginningpage189013-
dc.citation.endingpage189024-
dc.citation.publicationnameIEEE ACCESS-
dc.identifier.doi10.1109/ACCESS.2020.3032056-
dc.contributor.localauthorKim, Byung Hyung-
dc.contributor.localauthorJo, Sung-Ho-
dc.contributor.localauthorChoi, Sunghee-
dc.contributor.nonIdAuthorKoh, Seunghun-
dc.contributor.nonIdAuthorHuh, Sejoon-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorAffect-
dc.subject.keywordAuthorbrain lateralization-
dc.subject.keywordAuthorEEG-
dc.subject.keywordAuthorexplanatory efficacy-
dc.subject.keywordAuthorhuman-centric explainable artificial intelligence-
dc.subject.keywordAuthorinteractive explanation-
dc.subject.keywordAuthorworkload-
dc.subject.keywordPlusEMOTION-
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 3 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0