Artificial Intelligence Regulation: A framework for governance
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At the deployment stage, legal frameworks impose requirements related to explainability, documentation, and human oversight.
“At this stage, legal frameworks impose requirements related to explainability, documentation, and human oversight.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
Regulatory frameworks for AI are grounded in fundamental values such as fairness, transparency, accountability, and long-term sustainability.
“Almeida et al. [52] emphasize that regulatory frameworks for AI are grounded in fundamental values such as fairness, transparency, accountability, and long-term sustainability.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
Many sustainability and legitimacy problems associated with AI originate at the design stage, making early regulatory intervention essential.
“Almeida et al. [52] argue that many sustainability and legitimacy problems associated with AI originate at the design stage, making early regulatory intervention essential.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
AI regulation must be dynamic, allowing for continuous learning, auditing, and regulatory adjustment as AI systems evolve.
“Almeida et al. [52] highlight that AI regulation must be dynamic, allowing for continuous learning, auditing, and regulatory adjustment as AI systems evolve.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
Legal and policy instruments require organizations to assess potential risks related to discrimination, environmental harm, opacity, or loss of human oversight.
“Legal and policy instruments require organizations to assess potential risks related to discrimination, environmental harm, opacity, or loss of human oversight.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
The effectiveness of AI models for ESG decision-making depends on a broader risk-based AI governance and regulatory lifecycle framework.
“While different AI models (NLP, ANN, tree-based models, XAI, and hybrid systems) offer distinct advantages and limitations for ESG decision-making, their effectiveness depends on a broader risk-based AI governance and regulatory lifecycle framework.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
AI regulation should be understood as a multi-stage policy process covering the entire lifecycle of AI systems, from design to post-deployment monitoring.
“According to Almeida et al. [52], AI regulation should be understood as a multi-stage policy process covering the entire lifecycle of AI systems, from design to post-deployment monitoring.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
The legitimacy of AI-enabled decision-making in sustainable business models is positively associated with the transparency, explainability, and governance of AI systems.
“The legitimacy of AI-enabled decision-making in sustainable business models is positively associated with the transparency, explainability, and governance of AI systems.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
AI improves the efficiency of sustainable business model decision-making when deployed as a decision-support system that complements, rather than substitutes, human managerial judgment.
“AI improves the efficiency of sustainable business model decision-making when deployed as a decision-support system that complements, rather than substitutes, human managerial judgment.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
AI-enabled ESG decision-making operates not merely as a technological optimization tool but as part of a legally and institutionally structured governance process that shapes how firms design, implement, and control AI within sustainable business models.
“AI-enabled ESG decision-making therefore operates not merely as a technological optimization tool, but as part of a legally and institutionally structured governance process that shapes how firms design, implement, and control AI within sustainable business models [52].”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
AI enhances decision effectiveness in sustainable business models when AI systems are explicitly aligned with ESG objectives rather than purely economic performance metrics.
“AI enhances decision effectiveness in sustainable business models when AI systems are explicitly aligned with environmental, social, and governance (ESG) objectives rather than purely economic performance metrics.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
AI contributes to sustainable business model innovation when conceptualized as a dynamic capability that enables continuous adaptation to environmental and social complexity.
“AI contributes to sustainable business model innovation when conceptualized as a dynamic capability that enables continuous adaptation to environmental and social complexity.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [52]
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Responsible Artificial Intelligence Governance: A Review and Research Framework
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- Authors
- de Almeida, P. G. R., dos Santos, C. D., & Farias, J. S.
- Venue
- Ethics and Information Technology
- Published
- 2021-04-21
- Volume
- 23
- Issue
- 3
- Pages
- 505-525
- ISSN
- 1388-1957 (ISSN-L)
- 1572-8439
- OpenAlex
- W3154266813
- Semantic Scholar
- 4352ebf809d71e574ff96fcb593512803680e7d7
- Cited by
- 395
- References
- 83
- Citations by year
- 2026: 118 · 2025: 160 · 2024: 84 · 2023: 17 · 2022: 13 · 2021: 3
- FWCI
- 36.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ethics and Information Technology (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Business, Management and Accounting › Strategy and Management › Regulation and Compliance Studies
- Keywords
- AI regulation, AI governance, policy process, conceptual framework, stakeholder analysis, fairness, sustainability, freedom, agile governance, AI ethics, social values, AI legislation
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