Exploring an AI-Based Algorithmic Framework for Ethical Decision-Making in Human Resource Management Procedures

Abstract

In today’s dynamic and rapidly evolving workplace environments, ensuring ethical decision-making in Human Resource Management (HRM) procedures is crucial for organizational success and employee well-being. This research paper delves into the exploration of an innovative AI-based algorithmic framework designed to facilitate ethical decision-making processes within HRM contexts. Traditional approaches to ethical decision-making often rely on subjective judgement and are susceptible to biases, leading to inconsistent outcomes and potential ethical dilemmas. Leveraging the capabilities of artificial intelligence (AI), this framework integrates ethical principles, organizational values, and legal regulations into a structured algorithmic model. The proposed framework consists of several key components, including data collection and analysis, algorithm development, and decision support mechanisms. By harnessing AI technologies such as machine learning and natural language processing, the framework aims to enhance the objectivity, transparency, and consistency of ethical decision-making in HRM procedures. Moreover, the framework incorporates dynamic learning capabilities, allowing it to adapt and evolve based on feedback and real-world experiences. Through iterative refinement and continuous improvement, the algorithmic framework strives to address emerging ethical challenges and complexities in HRM practices. This research paper presents a comprehensive overview of the theoretical foundations, design principles, and implementation strategies of the AI-based algorithmic framework. Drawing on interdisciplinary insights from ethics, psychology, and computer science, the paper elucidates the potential benefits and implications of adopting such a framework in organizational contexts. Furthermore, the paper discusses potential ethical considerations and challenges associated with the deployment of AI technologies in HRM decision-making processes.