CHALLENGES IN ALGORITHMIC HUMAN RESOURCE MANAGEMENT: ANALYSIS AND FRAMEWORK

Abstract

Workplace in the twenty-first century workplace is nothing like the traditional systems, they are going through some serious transformation/upgrade rapidly due to advancement of artificial intelligence (AI), machine learning algorithms, and data-driven decision systems. These practices are completely taking over the human resource management functions. These computational operations in HRM is referred to as Algorithmic HRM, this has changed the nature of work, changed the employees experience for the better, and aligns the responsibilities of HR professionals more with the organization’s strategies. While algorithmic systems promise improved efficiency, objectivity, and scalability in managing human capital. AI also introduce a collection of previously left out challenges that carry large impact for employee well-being, organizational trust, and workplace equity.

This research project critically examines the challenges rooted in Algorithmic HRM with particular attention to the psychological and organizational functions that traditional HR practices has given less attention to. In the centre of this paper is the concept of emotional labour, which is theorized by sociologist Arlie Hochschild (1983) as the management of feeling to create a publicly observable facial and bodily display, which suggest that the work environments created by algorithms have become more complex to evaluate. When workers interact with customers through digital platforms which is monitored by AI, or when performance is evaluated by AI systems which is not capable of recognizing emotional context of the humans, the invisible psychological labour of emotion management becomes even more complex and it could be overlooked very easily by the organization.

Focusing exclusively on secondary data sources including peer-reviewed journal articles listed in Scopus and Web of Science, institutional reports from the International Labour Organization (ILO), McKinsey Global Institute, Deloitte, and World Health Organization (WHO), as well as landmark academic books and real-world case studies from platform economy organizations such as Uber, Amazon, and Swiggy, this study is based on a qualitative secondary research design which uses thematic synthesis methodology.

The analysis identifies six primary challenge areas in Algorithmic HRM: (1) algorithmic lack of transparency and the decline of procedural justice; (2) increased emotional labour demands in digitally supervised work environments; (3) the multiplication of algorithmic bias and partial decision making; (4) the breakdown of psychological safety and organizational trust; (5) the systematic promotion of burnout through automated performance pressure; and (6) the shortcoming of existing HR governance frameworks in addressing these current risks.

Taking the challenges in the account, this study make an innovative theoretical contribution to overcome and manage the challenges by suggesting the Integrated Algorithmic-Human Emotional Balance Framework (IAHEBF). IAHEBF is a conceptual model which is designed to guide HR professionals and leaders in making the HR systems more coherent algorithmically and better equipped to handle all the possible situations, by doing so it protects human dignity, support emotional well being, and maintain ethics in the governance of the organization. The framework takes in account the variables across four areas (1) algorithmic control parameters, (2) emotional labour dimensions, (3) organizational HR interventions, and (4) employee outcome indicators. These variables ensures that the HR systems remain theoretically grounded and also practically applicable in the real time scenarios. This framework ensures responsible and ethical algorithmic HRM across all the functions.

This study gives us findings which will be very incidental for future studies in this domain. The findings will be useful for HR researchers, policy makers, technology designers, and HR professionals and leaders working in the humanly aspects and ethical concepts of AI adoption in the dynamic workplace. Algorithmic processes are widely used in talent acquisition, performance management, workforce scheduling, and employee engagement. With the wide adoption it has become almost a necessity to design algorithmic systems which are more equipped with emotional intelligence, ethics, transparency and values that are core to the humans.