Bias in AI-Driven Performance Management Systems: Ethical and Practical Considerations

In the current world where artificial intelligence (AI) is employed in evaluating employees’ performance, there has been a raised concern on the issues of bias and fairness. This research paper aims at exploring the systematic bias of AI-based performance assessment models based on age, education level, and departmental status. The present work is based on the large-scale dataset of employee performance and reveals measurement, representation, aggregation, and historical bias in AI-based assessments. In its current state, AI discriminates employee competencies based on the set categories and replicates the traditional organizational hierarchy. This study underlines the importance of the development of better and more sophisticated methods for AI assessment that take into consideration factors such as fairness, interpretability of the algorithm and context awareness. The recommendations presented will be useful to organisations and policymakers who wish to reduce bias arising from the use of artificial intelligence in performance management systems.