Toward Effective AI Governance: A Review of Principles
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908384269172736 |
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| author | Ribeiro, Danilo Rocha, Thayssa Pinto, Gustavo Cartaxo, Bruno Amaral, Marcelo Davila, Nicole Camargo, Ana |
| author_facet | Ribeiro, Danilo Rocha, Thayssa Pinto, Gustavo Cartaxo, Bruno Amaral, Marcelo Davila, Nicole Camargo, Ana |
| contents | Artificial Intelligence (AI) governance is the practice of establishing frameworks, policies, and procedures to ensure the responsible, ethical, and safe development and deployment of AI systems. Although AI governance is a core pillar of Responsible AI, current literature still lacks synthesis across such governance frameworks and practices. Objective: To identify which frameworks, principles, mechanisms, and stakeholder roles are emphasized in secondary literature on AI governance. Method: We conducted a rapid tertiary review of nine peer-reviewed secondary studies from IEEE and ACM (20202024), using structured inclusion criteria and thematic semantic synthesis. Results: The most cited frameworks include the EU AI Act and NIST RMF; transparency and accountability are the most common principles. Few reviews detail actionable governance mechanisms or stakeholder strategies. Conclusion: The review consolidates key directions in AI governance and highlights gaps in empirical validation and inclusivity. Findings inform both academic inquiry and practical adoption in organizations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23417 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Effective AI Governance: A Review of Principles Ribeiro, Danilo Rocha, Thayssa Pinto, Gustavo Cartaxo, Bruno Amaral, Marcelo Davila, Nicole Camargo, Ana Software Engineering Artificial Intelligence Artificial Intelligence (AI) governance is the practice of establishing frameworks, policies, and procedures to ensure the responsible, ethical, and safe development and deployment of AI systems. Although AI governance is a core pillar of Responsible AI, current literature still lacks synthesis across such governance frameworks and practices. Objective: To identify which frameworks, principles, mechanisms, and stakeholder roles are emphasized in secondary literature on AI governance. Method: We conducted a rapid tertiary review of nine peer-reviewed secondary studies from IEEE and ACM (20202024), using structured inclusion criteria and thematic semantic synthesis. Results: The most cited frameworks include the EU AI Act and NIST RMF; transparency and accountability are the most common principles. Few reviews detail actionable governance mechanisms or stakeholder strategies. Conclusion: The review consolidates key directions in AI governance and highlights gaps in empirical validation and inclusivity. Findings inform both academic inquiry and practical adoption in organizations. |
| title | Toward Effective AI Governance: A Review of Principles |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2505.23417 |