Managing Transparency Fairness Accountability in AI for Sustainable Human Resource Management

The proliferation of Artificial Intelligence (AI) in Human Resource Management (HRM) offers significant efficiencies but concurrently introduces critical ethical challenges regarding transparency, fairness, and accountability, thereby impacting sustainable workforce management. The novelty of this study lies in its exploration of strategies for effectively managing these ethical dimensions in AI-driven HRM, with a focus on identifying practical solutions to mitigate algorithmic bias and enhance transparency. The research focuses on identifying ethical issues, such as algorithmic bias and lack of transparency in AI-assisted recruitment, performance evaluation, and overall employee management. Adopting a Systematic Literature Review (SLR) methodology, this paper analyzes recent publications (2019-2024) from Scopus to synthesize existing knowledge on AI ethics in HRM. Key findings reveal a significant lack of standardized best practices and audit trails, with opaque AI-driven decisions often undermining trust and fairness. If not properly designed, AI algorithms can perpetuate biases embedded in historical data, leading to discriminatory outcomes, as evidenced by cases at companies like Amazon and HireVue. The study concludes that organizations must proactively implement robust governance frameworks, including ethics training, the development of transparent and fair-by-design algorithms, regular audits, and mechanisms for human oversight. Integrating frameworks such as Fairness, Accountability, and Transparency (FAT) and Ethical AI by Design is crucial for ensuring that AI applications in HRM are ethically sound, legally compliant, and contribute to sustainable and equitable workforce management.