Beyond aversion – principles of appropriate algorithmic decision-making in human resource management

Abstract

As algorithmic decision-making (ADM) becomes increasingly embedded in human resource management (HRM), concerns such as a lack of fairness and accountability raise urgent questions about its appropriateness. This study addresses the need for ADM evaluation by developing a coherent framework of principles grounded in the task-technology fit approach. It elaborates a balanced triad of nine indispensable ADM principles—methodical (veracity, accuracy, validity), managerial (relevancy, quality, efficiency), and ethical (fairness, accountability, transparency)—and validates them through a systematic literature review of 126 ADM artifacts in HRM. The analysis reveals a troubling lack of attention to ethical and managerial dimensions, while even methodical aspects are often neglected—with the notable exception of accuracy. Building on these findings, the study outlines a forward-looking agenda to operationalize, calibrate, implement, evaluate, and codify ADM principles, ultimately promoting responsible, appropriate ADM in HRM that reflects an evaluative stance beyond mere aversion.