The introduction of Machine Learning (ML) to the system of performance evaluation is a game changer inhumanresourcemanagement (HRM). It is an empirical study that uses the synthesis of the secondary data in the forms of the industryreportsandpublished case studies to critically examine the efficacy, risks, and implementation pathways of the ML-driven performancetools.Wedevelop a conceptual map modelling the process of ML performance evaluation, which outlines the major points where thebiascanbeintroduced, and reducing it. We estimate the benefits that we can accrue to the corporate in terms of a 25-40%decrease inadministrationload and a 15-30% increase in the discovery of high-potential employees by looking at known cases of corporate implementation. Butitalso do provide empirical results of algorithmic bias, in which algorithms that are based upon biased historical data havereinforceddiscrimination, decreasing diversity in internal mobility by up to 20% in some examples recorded. The paper represents thetrade-offsofthe design of the ML systems with a sequence of equations: a utility function of adoption and a loss function that includesfairnessconstraints. Keywords: Machine Learning, Performance Evaluation, Algorithmic Bias, Human Resource Management, Empirical Research, EthicalAI, Implementation Framework, Secondary Data Analysis