Governance of Artificial Intelligence: Public Policy and Self-Regulatory Frameworks for Consequential Decision-Making

Organizations have increasingly relied on artificial intelligence (AI) and machine learning algorithms to generate predictions that feed into consequential decision-making. While AI has augmented some functional aspects of organizations, decision makers face challenges and risks when the use of AI technologies affects stakeholder wellbeing such as those pertaining to employee selection, employee performance evaluation, medical recommendation, and insurance claim rejection. Drawing on real-world examples of employment and healthcare decisions, this chapter of the Handbook on Artificial Intelligence and Strategy provides an overview of the promise and the peril of using AI. The chapter highlights the public policy and self-regulatory frameworks governing AI in decision-making and suggests ways that organizations can reap the benefits of AI through not only risk avoidance but also value creation. The overview informs scholars, professionals, and regulators about how AI governance may be designed to improve stakeholder wellbeing and the quality of decision-making at organizations.