Abstract
PurposeThis research analyzed the existing academic and grey literature concerning the technologies and practices of people analytics (PA), to understand how ethical considerations are being discussed by researchers, industry experts and practitioners, and to identify gaps, priorities and recommendations for ethical practice.Design/methodology/approachAn iterative “scoping review” method was used to capture and synthesize relevant academic and grey literature. This is suited to emerging areas of innovation where formal research lags behind evidence from professional or technical sources.FindingsAlthough the grey literature contains a growing stream of publications aimed at helping PA practitioners to “be ethical,” overall, research on ethical issues in PA is still at an early stage. Optimistic and technocentric perspectives dominate the PA discourse, although key themes seen in the wider literature on digital/data ethics are also evident. Risks and recommendations for PA projects concerned transparency and diverse stakeholder inclusion, respecting privacy rights, fair and proportionate use of data, fostering a systemic culture of ethical practice, delivering benefits for employees, including ethical outcomes in business models, ensuring legal compliance and using ethical charters.Research limitations/implicationsThis research adds to current debates over the future of work and employment in a digitized, algorithm-driven society.Practical implicationsThe research provides an accessible summary of the risks, opportunities, trade-offs and regulatory issues for PA, as well as a framework for integrating ethical strategies and practices.Originality/valueBy using a scoping methodology to surface and analyze diverse literatures, this study fills a gap in existing knowledge on ethical aspects of PA. The findings can inform future academic research, organizations using or considering PA products, professional associations developing relevant guidelines and policymakers adapting regulations. It is also timely, given the increase in digital monitoring of employees working from home during the Covid-19 pandemic.
Highlights
People analytics (PA) is an emerging area of innovation which, it draws on traditional principles of human resources management (HRM), represents a seismic shift in the power of organizations and their leaders to understand, shape and strategically optimize their workforce (e.g. Fitz-Enz and Mattox, 2014)
Interest in digital ethics has risen at an exponential rate in the last few years, with governments, academics and the technology industry racing to create new ethical principles, manifestos, guidelines and frameworks
This is reflected in the results of recent meta-review of artificial intelligence (AI) ethics guidelines, published in the Nature journal (Jobin et al, 2019) whose authors remark on the variation in interpretation and the difficulty of translating principles into regulations and practices
Summary
People analytics (PA) is an emerging area of innovation which, it draws on traditional principles of human resources management (HRM), represents a seismic shift in the power of organizations and their leaders to understand, shape and strategically optimize their workforce (e.g. Fitz-Enz and Mattox, 2014). Fitz-Enz and Mattox, 2014) This shift arises from the use of digital and data science methods to harvest, analyze and visualize complex information about individual employees, teams, divisions and the workforce as a whole, to provide actionable insights. PA techniques are extending beyond in-work metrics to new areas hitherto outside the reach of human resource (HR) departments or managers, including the monitoring of employees’ personal emails, social media activity and interactions with digital devices, and apps These may be presented as a means of supporting the employee experience or enhancing “workplace wellness” whilst, providing 24/7 intelligence about location, activity, mood, health and social life Employee data are being used to train algorithms to modify or “shape” behavior in and outside of the workplace, such as through gamifying tasks and incentives (e.g. Cardador et al, 2017)
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