Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model
Abstract A broad range of companies around the world has welcomed artificial intelligence (AI) technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hystere...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-08-01
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| Series: | Financial Innovation |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40854-022-00436-4 |
| _version_ | 1827590322735022080 |
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| author | Kuang-Hua Hu Fu-Hsiang Chen Ming-Fu Hsu Gwo-Hshiung Tzeng |
| author_facet | Kuang-Hua Hu Fu-Hsiang Chen Ming-Fu Hsu Gwo-Hshiung Tzeng |
| author_sort | Kuang-Hua Hu |
| collection | DOAJ |
| description | Abstract A broad range of companies around the world has welcomed artificial intelligence (AI) technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis. This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies, which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment. To obtain this goal and inspired by a model ensemble, we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing, fuzzy set theory, and a multi-attribute decision making algorithm. The results display that the order of priority in improvement—(A) AI application strategy, (B) AI governance, (D) the human factor, and (C) data infrastructure and data quality—is based on the magnitude of their impact. This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment. |
| first_indexed | 2024-03-09T01:15:16Z |
| format | Article |
| id | doaj.art-0a4b1fe1eb894bdf81f1d2465ff3f277 |
| institution | Directory Open Access Journal |
| issn | 2199-4730 |
| language | English |
| last_indexed | 2024-03-09T01:15:16Z |
| publishDate | 2023-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Financial Innovation |
| spelling | doaj.art-0a4b1fe1eb894bdf81f1d2465ff3f2772023-12-10T12:28:50ZengSpringerOpenFinancial Innovation2199-47302023-08-019113110.1186/s40854-022-00436-4Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making modelKuang-Hua Hu0Fu-Hsiang Chen1Ming-Fu Hsu2Gwo-Hshiung Tzeng3School of Accounting, Finance and Accounting Research Center, Nanfang CollegeDepartment of Accounting, Chinese Culture UniversityDepartment of Business Management, National United UniversityGraduate Institute of Urban Planning, National Taipei UniversityAbstract A broad range of companies around the world has welcomed artificial intelligence (AI) technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis. This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies, which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment. To obtain this goal and inspired by a model ensemble, we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing, fuzzy set theory, and a multi-attribute decision making algorithm. The results display that the order of priority in improvement—(A) AI application strategy, (B) AI governance, (D) the human factor, and (C) data infrastructure and data quality—is based on the magnitude of their impact. This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment.https://doi.org/10.1186/s40854-022-00436-4Fuzzy multiple rule-based decision makingAuditingArtificial intelligenceRisk management |
| spellingShingle | Kuang-Hua Hu Fu-Hsiang Chen Ming-Fu Hsu Gwo-Hshiung Tzeng Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model Financial Innovation Fuzzy multiple rule-based decision making Auditing Artificial intelligence Risk management |
| title | Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model |
| title_full | Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model |
| title_fullStr | Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model |
| title_full_unstemmed | Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model |
| title_short | Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model |
| title_sort | governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule based decision making model |
| topic | Fuzzy multiple rule-based decision making Auditing Artificial intelligence Risk management |
| url | https://doi.org/10.1186/s40854-022-00436-4 |
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