The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity

Abstract

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.

Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development

“…Striving for more efficiency due to time and cost pressures and limited resources by simultaneously managing a large number of applications are among the main reasons for the increasing use of algorithmic decision-making in the selection context (Leicht-Deobald et al 2019). Organizations are increasingly using algorithmic decision tools, such as CV and résumé screening, telephone, or video interviews, providing an algorithmic evaluation (Lee and Baykal 2017; Mann and O'Neil 2016) before conducting face-to-face interviews (Chamorro-Premuzic et al 2016;van Esch et al 2019).…”

“…Companies increasingly rely on algorithmic decision-making to quantify and monitor their employees (Leicht-Deobald et al 2019). Personal records and internal performance evaluation are documented in firm systems.…”

“…Other goals are workforce forecasts (retention, leaves) and comprehension of employee's satisfaction indicators (Simbeck 2019;Silverman and Waller 2015). Typical data stored in HR information systems include information about the employees hired, the employee's pay and benefits, hours worked, and sometimes various performance-related measures (Leicht-Deobald et al 2019). Personal data, such as the number and age of children, marital status, and health information, are often available for the HR function (Simbeck 2019).…”

Justice perceptions of artificial intelligence in selection

“…There is limited research available regarding applicants' reactions to the use of AI by companies in hiring (Langer, König, & Fitili, 2018; Langer, König, & Papathanasiou, 2019; Lee, 2018; McCarthy et al., 2017) despite the fact that understanding applicant reactions is becoming increasingly important due to AI integration (Black & van Esch, 2020) and the ethical considerations of AI decision making (Leicht‐Deobald et al., 2019). Applicant reactions can be defined as the attitudes, affect, or cognitions applicants might have about a hiring process or selection tools (McCarthy et al., 2017; Ryan & Ployhart, 2000).…”

No stone left unturned? Towards a framework for the impact of datafication technologies on organizational control

“…We believe that one particular self-reinforcing driver is salient -an illusion of control through tracking technology (Neff & Nafus, 2016). There is a strong narrative of a strong belief in technology, which would rid us of human bias and allow us to control aspects that humans are unable to control due to a lack of expertise (Finlay, 2014;Leicht-Deobald et al, 2019;Strauss, Kristandl, & Quinn, 2015). Selftracking technologies promise the possibility of tracking and categorizing each essential aspect of human action and social interaction (Neff & Nafus, 2016).…”