Human AI Decision Making Ethical Governance Framework: Integrating Algorithmic Fairness and Managerial Intuition

Abstract

The increasing use of artificial intelligence (AI)-supported decision-making systems in critical areas such as healthcare, finance, and justice raises important concerns regarding ethics, bias, and fairness. This conceptual article proposes a holistic framework that integrates the principles of algorithmic fairness with the unique value of managerial intuition to ensure ethical governance in human–AI decision-making contexts. The interdisciplinary literature synthesis reveals that existing ethical principles often remain abstract and that a practical roadmap is needed. The proposed framework consists of five core components: (i) human-centered design and continuous oversight, (ii) transparency and explainability, (iii) bias management and mitigation, (iv) integration of managerial intuition, and (v) ethical governance and continuous adaptation. Together, these components safeguard ethical responsibility in human–AI collaboration, address algorithmic biases, and ensure that intuition serves as a “moral compass” under uncertainty. The article highlights the efficiency and consistency advantages of algorithmic objectivity while emphasizing that the contextual understanding, empathy, and ethical judgment capacity of human intuition creates a complementary force. This synergy is the key to decision-making processes that are not only efficient but also just and humane. The study provides a practical roadmap for policymakers, developers, and managers and proposes a research agenda for future empirical validation, cultural adaptations, and ethical governance models.