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dc.contributor.authorVice, Jordan Joshua
dc.contributor.supervisorMasood Khanen_US
dc.contributor.supervisorTele Tanen_US
dc.date.accessioned2023-03-13T06:10:48Z
dc.date.available2023-03-13T06:10:48Z
dc.date.issued2022en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/90847
dc.description.abstract

The rapid growth of artificial intelligence (AI) and machine learning (ML) solutions has seen it adopted across various industries. However, the concern of ‘black-box’ approaches has led to an increase in the demand for high accuracy, transparency, accountability, and explainability in AI/ML approaches. This work contributes through an accountable, explainable AI (AXAI) framework for delineating and assessing AI systems. This framework has been incorporated into the development of a real-time, multimodal affective state assessment system.

en_US
dc.publisherCurtin Universityen_US
dc.titleAccountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Moduleen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentSchool of Civil and Mechanical Engineeringen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidVice, Jordan Joshua [0000-0002-3951-1188]en_US


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