AI Ethics: A Bibliometric Analysis of AI Ethics in Business and Management

As artificial intelligence (AI) is integrated in business, management and society, ethical considerations have evolved from abstract concepts to pressing practical challenges. Research in fields like marketing, software engineering, education, finance or creative industries highlights a consistent gap between high-level ethical principles, such as transparency, fairness, accountability, privacy, and human dignity and their concrete implementation in organizational practice. Studies emphasize that effective AI ethics cannot be achieved only through technical design alone; it requires robust governance structures, cross-functional collaboration between managers and developers, ethics committees, and continuous education and training throughout the AI lifecycle. Empirical evidence suggests that translating ethics into specific rules, processes, and compliance functions can mitigate fear, foster trust, and enhance responsible innovation, although these efforts involve significant resource trade-offs and costs. Emerging frameworks, including ethical requirements canvases, circular models of ethics in action, auditing strategies, and licensing or rating systems, illustrate practical methods for operationalizing ethics and aligning them with business value. At the same time, critical perspectives reveal unresolved tensions around trust, power dynamics, creativity, authorship, automation, and human agency, along with diverse cultural and spiritual interpretations of AI’s moral significance. Bibliometric and conceptual analyses further identify key ethical issues and research gaps, underscoring the need for human-centric, context-sensitive, and globally coordinated approaches. In general, this body of work positions AI ethics not as a constraint on innovation, but as a strategic and societal imperative for building trustworthy, sustainable, and socially responsible AI systems that effectively serve both organizations and the broader public.