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dc.contributor.authorGoltz, Nachshon (Sean)en_NZ
dc.contributor.authorMayo, Michaelen_NZ
dc.coverage.spatialKing’s College, London, UKen_NZ
dc.date.accessioned2018-07-13T01:59:09Z
dc.date.available2017en_NZ
dc.date.available2018-07-13T01:59:09Z
dc.date.issued2017en_NZ
dc.identifier.citationGoltz, N., & Mayo, M. (2017). Enhancing regulatory compliance by using artificial intelligence text mining to identify penalty clauses in legislation. Presented at the MIREL 2017 - Workshop on ‘Mining and REasoning with Legal texts’, held in conjunction with the 16th International Conference on Artificial Intelligence and Law, King’s College, London, UK.en
dc.identifier.urihttps://hdl.handle.net/10289/11940
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.relation.urihttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=2977570
dc.rights© 2017 copyright with the authors.
dc.sourceMIREL 2017 - Workshop on 'Mining and REasoning with Legal texts', held in conjunction with the 16th International Conference on Artificial Intelligence and Lawen_NZ
dc.subjectRegulatory compliance
dc.subjectArtificial Intelligence
dc.subjectText mining
dc.subjectPenalties
dc.subjectMachine learning
dc.titleEnhancing regulatory compliance by using artificial intelligence text mining to identify penalty clauses in legislationen_NZ
dc.typeConference Contribution
pubs.elements-id195205
pubs.finish-date2017-06-16en_NZ
pubs.publisher-urlhttp://www.mirelproject.eu/MIRELws/#section-programen_NZ
pubs.start-date2017-06-12en_NZ


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