Artificial Intelligence-Augmented Decision-Making: Examining the Interplay Between Machine Learning Algorithms and Human Judgment in Organizational Leadership

Abstract

The paper discusses the bright and dark sides of the relationship between human judgment and AI-driven machine learning (ML) algorithms. While discussing important issues, such as algorithm aversion, automation bias, and trust, it probes into how AI improves decision-making efficiency through predictive accuracy, resource optimisation, and data-driven insights. Even as AI can revolutionise decision-making, its effective integration must balance algorithmic output and human judgment. The most critical challenges include automation bias resulting from over-reliance on advice given by AI and algorithm aversion driven by concerns related to AI failures. Open systems, explainable AI (XAI) frameworks, and user-centered design can help to engender confidence in AI systems and alleviate these issues. Accountability, equity, and prejudice concerns raise further ethical considerations with AI. The study proposed several tactics that might mitigate such challenges: audits of ethics, adherence to legal policy, and integration of the AI systems with the company’s values. It underlines the human-AI collaboration that will be increasingly necessary, as well as hybrid models for decision-making that bring algorithmic accuracy to human intuition. It follows the case study review and empirical findings with practical lessons for organisational leaders on ethics, best deployment practices for AI, and tactical ways to engender better collaboration and trust. The conclusion outlines the need to enhance the explainability features of AI, study cognitive dynamics in decision processes, and work out ethical schemata guiding leading positions for AI. Beyond providing a roadmap for organisations to leverage the interaction of human judgment and machine intelligence to drive and achieve more ethical and effective leadership outcomes, this paper tries to contribute to the ongoing debate on AI-augmented decision-making.