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Abstract As artificial intelligence (AI) transforms a wide range of sectors and drives innovation, it also introduces different types of risks that should be identified, assessed, and mitigated. Various AI governance frameworks have been released recently by governments, organizations, and companies to mitigate risks associated with AI. However, it can be challenging for AI stakeholders to have a clear picture of the available AI governance frameworks, tools, or models and analyze the most suitable one for their AI system. To fill the gap, we present the literature to answer key questions: WHO is accountable for AI systems’ governance, WHAT elements are being governed, WHEN governance occurs within the AI development life cycle, and HOW it is implemented through frameworks, tools, policies, or models. Adopting the systematic literature review (SLR) methodology, this study meticulously searched, selected, and analyzed 28 articles, offering a foundation for understanding different facets of AI governance. The analysis is further enhanced by categorizing artifacts of AI governance under team-level governance, organization-level governance, industry-level governance, national-level governance, and international-level governance. The findings of this study on existing AI governance solutions can assist research communities in proposing comprehensive AI governance practices.
Batool et al. (Tue,) studied this question.
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