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dc.contributor.authorGoltz, Nachshon (Sean)en_NZ
dc.contributor.authorMayo, Michaelen_NZ
dc.date.accessioned2022-02-07T22:56:34Z
dc.date.available2022-02-07T22:56:34Z
dc.date.issued2018en_NZ
dc.identifier.issn2575-5617en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/14739
dc.description.abstractAs regulatory compliance (or compliance governance) becomes ever more challenging, attempts to engage IT solutions and especially artificial intelligence (AI) are on the rise. This paper suggest that regulatory compliance can be enhanced by employing an AI model trained to identify penalty clauses in the regulations. The paper provides the theoretical basis of machine learning for text classification and presents a two stage experiment of (1) training multiple models and selecting the best one; and (2) employing a sliding window detection in order to identify penalty clauses in regulation. Results benchmarked using an algorithm based penalties API suggests further development is needed.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherFull Court Pressen_NZ
dc.relation.urihttps://heinonline.org/HOL/Page?handle=hein.journals/rail1&id=182&collection=journals&index=en_NZ
dc.rights© 2018 Full Court Press. Used with permission
dc.titleEnhancing regulatory compliance by using artificial intelligence text mining to identify penalty clauses in legislationen_NZ
dc.typeJournal Article
dc.relation.isPartOfRAIL: Robotics, Artificial Life & Lawen_NZ
pubs.begin-page175
pubs.elements-id203185
pubs.end-page191
pubs.issue3en_NZ
pubs.publication-statusPublished onlineen_NZ
pubs.volume1en_NZ


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