A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature review
As artificial intelligence (AI) is increasingly used in various industries, it becomes crucial for organizations to enhance their capabilities and maturity in adopting AI responsibly. This paper employs a mixed-method approach that combines topic modeling with manual content analysis to provide a co...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266709682300040X |
| _version_ | 1797398984944254976 |
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| author | Pouria Akbarighatar Ilias Pappas Polyxeni Vassilakopoulou |
| author_facet | Pouria Akbarighatar Ilias Pappas Polyxeni Vassilakopoulou |
| author_sort | Pouria Akbarighatar |
| collection | DOAJ |
| description | As artificial intelligence (AI) is increasingly used in various industries, it becomes crucial for organizations to enhance their capabilities and maturity in adopting AI responsibly. This paper employs a mixed-method approach that combines topic modeling with manual content analysis to provide a comprehensive review of the literature on AI maturity and readiness. The review encompasses an extensive corpus of 1451 papers, identifying the main themes and topics within this body of literature. Based on these findings, a subset of papers was selected and further analyzed to identify AI capabilities utilizing a sociotechnical lens. This further analysis led to the identification of foundational and responsible AI (RAI) capabilities. These capabilities have been integrated in a sociotechnical framework of capabilities for AI maturity models providing valuable insights for organizations and AI service providers and a basis for further research. |
| first_indexed | 2024-03-09T01:33:25Z |
| format | Article |
| id | doaj.art-a356d735c2b54e20bb027d5e77f364e3 |
| institution | Directory Open Access Journal |
| issn | 2667-0968 |
| language | English |
| last_indexed | 2024-03-09T01:33:25Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj.art-a356d735c2b54e20bb027d5e77f364e32023-12-09T06:08:38ZengElsevierInternational Journal of Information Management Data Insights2667-09682023-11-0132100193A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature reviewPouria Akbarighatar0Ilias Pappas1Polyxeni Vassilakopoulou2Department of Information Systems, University of Agder, Universitetsveien 25, 4604, Kristiansand, Norway; Corresponding author at: Department of Information Systems, University of Agder, Universitetsveien 25, 4604, Kristiansand, Norway.Department of Information Systems, University of Agder, Universitetsveien 25, 4604, Kristiansand, Norway; Department of Computer Science, Norwegian University of Science and Technology, Sem Sælandsvei 9, 7491, Trondheim, NorwayDepartment of Information Systems, University of Agder, Universitetsveien 25, 4604, Kristiansand, NorwayAs artificial intelligence (AI) is increasingly used in various industries, it becomes crucial for organizations to enhance their capabilities and maturity in adopting AI responsibly. This paper employs a mixed-method approach that combines topic modeling with manual content analysis to provide a comprehensive review of the literature on AI maturity and readiness. The review encompasses an extensive corpus of 1451 papers, identifying the main themes and topics within this body of literature. Based on these findings, a subset of papers was selected and further analyzed to identify AI capabilities utilizing a sociotechnical lens. This further analysis led to the identification of foundational and responsible AI (RAI) capabilities. These capabilities have been integrated in a sociotechnical framework of capabilities for AI maturity models providing valuable insights for organizations and AI service providers and a basis for further research.http://www.sciencedirect.com/science/article/pii/S266709682300040XArtificial intelligenceMaturity modelResponsible AI capabilitiesTopic modelingSociotechnical |
| spellingShingle | Pouria Akbarighatar Ilias Pappas Polyxeni Vassilakopoulou A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature review International Journal of Information Management Data Insights Artificial intelligence Maturity model Responsible AI capabilities Topic modeling Sociotechnical |
| title | A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature review |
| title_full | A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature review |
| title_fullStr | A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature review |
| title_full_unstemmed | A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature review |
| title_short | A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature review |
| title_sort | sociotechnical perspective for responsible ai maturity models findings from a mixed method literature review |
| topic | Artificial intelligence Maturity model Responsible AI capabilities Topic modeling Sociotechnical |
| url | http://www.sciencedirect.com/science/article/pii/S266709682300040X |
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