The Role of AI in Automating HR Performance Reviews: Efficiency vs Fairness

Abstract

Performance reviews have been a core part of HR for a long time but the way most companies still do them is pretty outdated (Chamorro-Premuzic et al., 2020). Managers usually rely on their own memory and judgment which brings in problems like favoritism and recency bias (Sadeghi, 2024). AI is now being used to change this by bringing in machine learning and natural language processing to evaluate employees based on actual work data instead of just opinions (Rsisinternational, 2024). Research shows that AI can improve HR process efficiency by 57 to 80 percent which is a massive jump (Advances in Consumer Research, 2025). But it is not all good news. A systematic review of 43 studies found that while AI can standardize evaluations it also carries the risk of embedding old biases into new systems (Management Review Quarterly, 2025). Employees have their own concerns too. A survey of 306 workers found that they only trust AI evaluations when the criteria being used clearly relate to their actual job tasks (Cogent Business and Management, 2025). On the other hand when people already feel their human managers are biased they tend to prefer AI as a fairer alternative (Cha, 2025). The fairness question is tricky because even the people building these systems sometimes cannot fully explain how the algorithms arrive at a particular score (Hughes et al., 2025). This paper takes a look at what the current research says about AI in performance reviews covering both the efficiency gains and the fairness concerns and tries to give a balanced view of where things actually stand right now.

Keywords: NET Framework, .NET Core, Enterprise Application, Migration, Performance Optimization, Cross-Platform Development, Web Application Development, API Integration, Software Modernization, System Architecture.