Towards an integrated framework for artificial intelligence governance

• Main Authors: ESCHENBRENNER, Brenda, NAH, Fiona Fui-hoon, SIAU, Keng, CHEN, Langtao, QIAN, Yuzhou
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Artificial intelligence; Analytic theory; Ethical concerns; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/9502
• Institution: Singapore Management University

Artificial intelligence (AI) is being developed and adopted by many organizations throughout the world. As the potential of AI is being leveraged, many opportunities are being realized and continue to emerge. However, potential issues need to be addressed (Wang and Siau, 2019), such as ethical and legal concerns (Siau and Wang, 2020), making an AI governance framework paramount (Wang and Siau, 2018; Chen et al., 2022). To address this need, we propose an integrated AI governance framework based on an analysis of existing AI frameworks from different regions of the world (i.e., United States, European Commission, Singapore, and Hong Kong). More specifically, we systematically analyzed these frameworks, juxtaposed the frameworks to identify similarities and differences, which allowed us to identify the core components of AI governance, and proposed an integrated framework for AI governance that adheres to the characteristics of analytic theory (Gregor, 2006). The proposed AI governance framework encompasses both Strategic as well as Tactical and Operational components. There is an overarching theme that crosses the Strategic, Tactical, and Operational components that we termed Stakeholder Communication, Interaction, and Engagement. The integrated framework can be utilized by practitioners as guidelines for their AI endeavors and it can also serve as a foundation to guide future AI governance research. Moving forward, we plan to conduct case studies on AI governance frameworks in organizations and study their impacts on AI success. Future research also includes extending our proposed AI governance framework and fine-tuning it to fit unique organizational characteristics or specific sectors of industry.