Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module
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 approac...
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| Format: | Thesis |
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Curtin University
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/90847 |
| _version_ | 1848765443107454976 |
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| author | Vice, Jordan Joshua |
| author_facet | Vice, Jordan Joshua |
| author_sort | Vice, Jordan Joshua |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:35:20Z |
| format | Thesis |
| id | curtin-20.500.11937-90847 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:35:20Z |
| publishDate | 2022 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-908472023-03-13T06:10:48Z Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module Vice, Jordan Joshua 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. 2022 Thesis http://hdl.handle.net/20.500.11937/90847 Curtin University fulltext |
| spellingShingle | Vice, Jordan Joshua Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module |
| title | Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module |
| title_full | Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module |
| title_fullStr | Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module |
| title_full_unstemmed | Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module |
| title_short | Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module |
| title_sort | accountable, explainable artificial intelligence incorporation framework for a real-time affective state assessment module |
| url | http://hdl.handle.net/20.500.11937/90847 |