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.

The pervasive presence and wide-ranging variety of artificial intelligence (AI) systems underscore the necessity for inclusivity and diversity in their design and implementation, to effectively address critical issues of fairness, trust, bias, and transparency. However, diversity and inclusion (D&I) considerations are significantly neglected in AI systems design, development, and deployment. Ignoring D&I in AI systems can cause digital redlining, discrimination, and algorithmic oppression, leading to AI systems being perceived as untrustworthy and unfair. Therefore, we conducted a systematic literature review (SLR) to identify the challenges and their corresponding solutions (guidelines/ strategies/ approaches/ practices) about D&I in AI and about the applications of AI for D&I practices. Through a rigorous search and selection, 48 relevant academic papers published from 2017 to 2022 were identified. By applying open coding on the extracted data from the selected papers, we identified 55 unique challenges and 33 unique solutions in addressing D&I in AI. We also identified 24 unique challenges and 23 unique solutions for enhancing D&I practices by AI. The result of our analysis and synthesis of the selected studies contributes to a deeper understanding of diversity and inclusion issues and considerations in the design, development and deployment of the AI ecosystem. The findings would play an important role in enhancing awareness and attracting the attention of researchers and practitioners in their quest to embed D&I principles and practices in future AI systems. This study also identifies important gaps in the research literature that will inspire future direction for researchers.