Automated Performance Evaluation and Due Process in Disciplinary Procedures

Abstract

Digital technologies that are now increasingly being adopted in HRM have changed the way in which performance of employees is measured, monitored, evaluated, and managed. The systems for Automated Performance Evaluation (APE) make use of such technologies as rule-based scoring, key performance indicators (KPIs), machine learning, employee monitoring software, natural language processing (NLP), and Human Resource Information Systems (HRIS) for evaluating the behavior and performance of employees. While there is an array of advantages that the implementation of these technologies may bring, there are significant issues associated with the use of these technologies for disciplining. The score that is assigned by algorithms may have a bearing on any warning issued to the employee, development of performance improvement plan, suspension, promotion, termination, or any other decision affecting the employment. The study seeks to explore the link between automated performance evaluation and due process in workplace discipline proceedings. Concepts such as APE, algorithmic management, natural justice, and organizational justice will be developed. The study shall also explore technologies used in automatic evaluation of employee performances and how such technologies could be used in various phases of discipline processes. Special consideration shall be given to the right to notice, being given a chance to hear oneself, access to information/evidence, impartiality of decisions, explanations of algorithmic decisions, and the possibility of human review. Legal considerations include Indian labour laws and employment law principles, the Digital Personal Data Protection Act, 2023 and the Digital Personal Data Protection Rules, 2025 as well as the international approaches such as the General Data Protection Regulation (GDPR), EU Artificial Intelligence Act among others. The paper discusses some of the challenges associated with automated evaluation of employee performance such as opacity of algorithms, discrimination or bias, inaccuracies in the use of data, failure to take context into consideration, over monitoring, accountability issues, and power asymmetry. Finally, an automated evaluation process that is consistent with due process is developed.