Organizations increasingly rely on algorithm-based HR decision-making to monitor their employees. This trend is reinforced by the technology industry claiming that its decision-making tools are efficient and objective, downplaying their potential biases. In our manuscript, we identify an important challenge arising from the efficiency-driven logic of algorithm-based HR decision-making, namely that it may shift the delicate balance between employees’ personal integrity and compliance more in the direction of compliance. We suggest that critical data literacy, ethical awareness, the use of participatory design methods, and private regulatory regimes within civil society can help overcome these challenges. Our paper contributes to literature on workplace monitoring, critical data studies, personal integrity, and literature at the intersection between HR management and corporate responsibility.

Technological advances in the field of artificial intelligence (AI) are heralding a new era of analytics and data-driven decision-making. Organisations increasingly rely on people analytics to optimise human resource management practices in areas such as recruitment, performance evaluation, personnel development, health and retention management. Recent progress in the field of AI and ever-increasing volumes of digital data have raised expectations and contributed to a very positive image of people analytics. However, transferring and applying the efficiencydriven logic of analytics to manage humans carries numerous risks, challenges, and ethical implications. Based on a theorising review our paper analyses perils that can emerge from the use of people analytics. By disclosing the underlying assumptions of people analytics and offering a perspective on current and future technological advancements, we identify six perils and discuss their implications for organisations and employees. Then, we illustrate how these perils may aggravate with increasing analytical power of people analytics, and we suggest directions for future research. Our theorising review contributes to information system research at the intersection of analytics, artificial intelligence, and human-algorithmic management.

The authors thank the anonymous reviewers for their valuable and constructive comments as well as for their helpful advice to improve the quality of the article. Our special thanks go to Claudio Kick, whose remarkable efforts in data collection have contributed to the overall success of the research project. Also, we are truly grateful for the excellent comments and critical thinking of Sim Sitkin and Chet Miller from which our paper benefited significantly. We are grateful for the dedicated efforts of Giulia Solinas and the entire editorial team of this special issue enabling such a fruitful exchange of ideas during the review and publication process. Finally, we thank our expert sounding board for the valuable insights as well as the Swiss National Science Foundation (NFP75) for the funding supporting this work.

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