Artificial Intelligence in Human Resources Management: Challenges and a Path Forward

Abstract

There is a substantial gap between the promise and reality of artificial intelligence in human resource (HR) management. This article identifies four challenges in using data science techniques for HR tasks: complexity of HR phenomena, constraints imposed by small data sets, accountability questions associated with fairness and other ethical and legal constraints, and possible adverse employee reactions to management decisions via data-based algorithms. It then proposes practical responses to these challenges based on three overlapping principles—causal reasoning, randomization and experiments, and employee contribution—that would be both economically efficient and socially appropriate for using data science in the management of employees.

Justice perceptions of artificial intelligence in selection

“…With technology advancing at an accelerated pace, practitioners are embracing new recruitment and selection methods, making technological aspects of the staffing process that affect applicant reactions of particular relevance. One such technology which is increasingly being utilized in selection is Artificial Intelligence (AI), which can be defined as “a broad class of technologies that allow a computer to perform tasks that normally require human cognition, including adaptive decision making” (Tambe, Cappelli, & Yakubovich, 2019, p. 16). AI techniques have the potential to streamline various HR activities.…”

“…One taxonomy by Tambe et al. (2019) describes the “AI Life Cycle” in terms of operations, data generation, machine learning, and decision making, with each stage being more advanced applications of AI. For example, operations deal with administrative tasks of HR, whereas data generation involves HR information systems and similar kinds of database management.…”

Estimating the Impact of ‘Humanizing’ Customer Service Chatbots

“…Humans and Algorithmic Decision Making. Several emerging studies in Human Resources(Tambe et al 2019), Economics(Kleinberg et al 2017) and Psychology(Dietvorst et al 2015, 2018, Logg et al 2019 have investigated how humans respond to algorithmic outcomes Dietvorst et al (2015). find that in general humans are averse to forecasts made by an algorithm, even when they outperform their less accurate human counter-parts Dietvorst et al (2018).…”

“…further this line of inquiry and find that algorithmic aversion can be reduced when individuals have the ability to manipulate and make adjustments to the algorithm. Similarly,Tambe et al (2019) theorize that employees will be less accepting of algorithmically determined shift decisions than those determined by a supervisor as they could potentially feel less involved in the decision. Interestingly,Tambe et al (2019), further discusses an anecdote from Uber, describing that individuals negatively respond to surge pricing when they believe it is set by an algorithm.Contrasting these findingsLogg et al (2019) find that individuals can be appreciative of algorithmic judgements in numeric forecasts and recommendations for dating and music, as opposed to those made by humans.…”

“…Similarly,Tambe et al (2019) theorize that employees will be less accepting of algorithmically determined shift decisions than those determined by a supervisor as they could potentially feel less involved in the decision. Interestingly,Tambe et al (2019), further discusses an anecdote from Uber, describing that individuals negatively respond to surge pricing when they believe it is set by an algorithm.Contrasting these findingsLogg et al (2019) find that individuals can be appreciative of algorithmic judgements in numeric forecasts and recommendations for dating and music, as opposed to those made by humans. In addition,Logg et al (2019) find, similar toDietvorst et al (2018), that Author:12 Article submitted to Information Systems Research; manuscript no.…”