Ethical Artificial Intelligence in Nursing Workforce Management and Policymaking: Bridging Philosophy and Practice

<b>Background:</b> Despite artificial intelligence's (AI) transformative potential in healthcare, nursing workforce scholarship lacks a cohesive theoretical foundation and well-established philosophical stances to guide safe yet ethical, effective yet efficient, and sustainable AI integration into nursing workforce management and policymaking. This gap poses significant challenges in leveraging AI's benefits while mitigating potential risks and inequities. <b>Aim:</b> This paper aims to (1) present a philosophical discourse centered on Park's optimized nurse staffing (Sweet Spot) theory and (2) propose a novel theoretical framework with specific methodologies for ethical AI-equipped nursing workforce management and policymaking while providing its philosophical underpinnings. <b>Method:</b> A rigorous philosophical discourse was performed through <i>theoretical triangulation</i>, grounded in Park's Optimized Nursing Staffing (Sweet Spot) Estimation Theory. This approach synthesizes diverse philosophical perspectives to create a robust foundation for ethical AI integration in nursing workforce management and policymaking. <b>Discussion:</b> The novel theoretical framework introduces its well-established philosophical underpinnings, bridging <i>moderate realism</i> with <i>post-positivism</i> and <i>contextualism</i>, for ethical AI-equipped nursing workforce management and policymaking. The framework also provides practical solutions for ethical AI integration while ensuring equity and fairness in nursing workforce practices. This approach consequently offers a groundbreaking pathway toward sustainable AI-equipped nursing workforce management and policymaking that balances safety, ethics, effectiveness, and efficiency. <b>Implication on Nursing Management:</b> This paper is the first to present a theoretical framework for ethically integrating AI into nursing workforce management and policymaking, grounded in its robust philosophical underpinnings. It stands out for its creativity and originality, making a significant contribution by opening new avenues for emerging research and development at the intersection of AI and healthcare. Specifically, the framework serves as a practical and pivotal resource for researchers, policymakers, and healthcare administrators navigating the complex landscape of AI integration in nursing workforce management and policymaking. Above all, it is worthwhile in that this paper contributes to the broader intellectual discourse in a thought-provoking and timely manner by addressing AI's inherent limitations in healthcare through a theoretical framework embedded in human philosophical and ethical deliberation. Unlike the current practice where AI safety and ethical risk assessment are conducted after AI solutions have been developed, this approach provides proactive guidance. Thereby, it lays the crucial groundwork for future empirical studies and practical implementations toward desirable healthcare decision-making.