Artificial intelligence (AI) systems are increasingly integrated into domains such as hiring, credit scoring, and marketing, policing, raising ethical challenges that extend across industries and disciplines. This keynote examines the overarching challenges of AI, including bias, opacity, contested notions of fairness, and declining stakeholder trust. Building on recent scholarship, the session introduces the Mishra Khazanchi TAR Framework (Trust, Accountability, Representation) as a conceptual tool for analyzing fairness throughout the AI lifecycle. Real-world business cases are used to demonstrate how ethical theories-utilitarianism, rights-based approaches, justice, and virtue ethics-ofer complementary perspectives for evaluation and decision-making. In parallel, the talk briefly addresses pedagogical strategies for teaching AI ethics in academic and professional programs, including business, information systems, and technology education. Finally, pedagogical strategies informed by learning theories are presented to show how AI ethics can be taught and embedded in business contexts.
The authors have not employed any Generative AI tools.