AI and Emotional Analytics in Employee Performance Evaluation

Abstract

With the advent of Artificial Intelligence (AI), the HR evaluation landscape is changing with the power to analyze employee performance data. Artificial Intelligence (AI) in HRM is changing the traditional method of measuring employee performance by organizations' ability to measure measurable work outcomes and behavioural indicators. New uses span emotional analytics, which offer an opportunity to measure employees' emotions, engagement, stress, communication patterns and interactions in the workplace. The study explores the feasibility of AI-powered emotional analytics in enhancing the process of evaluating employee performance and aiding better decision-making in HR. The study will explore the possibility of employing emotional data collected from the right sources for the company (employee feedback, communication, sentiment analysis, engagement, etc.) to complement the traditional measures. These analytical tools can aid managers to identify trends in behaviour, provide ongoing insights, and gain insight into areas that affect productivity, motivation, teamwork and satisfaction. At the same time, there are numerous issues related to privacy, informed consent, data security, algorithmic bias, transparency, and over-surveillance that surround the use of emotional analytics. The study therefore emphasizes the need for both technology efficiency and human factors and moral issues. It believes that AI should help in decision making, not make decisions. A responsible approach featuring clear and transparent policies, validated analytical models and the involvement of employees and adequate human oversight can help to achieve the credibility and fairness of AI-based performance assessment. The study ends with a call for AI and emotional analytics algorithms to be employed in a supportive way to implement an employee performance management system that is more continuous, personalized and evidence-based, while respecting the autonomy and dignity of employees.