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RESEARCH PAPER

Highly Accurate, But Still Discriminatory A Fairness Evaluation of Algorithmic Video Analysis in the Recruitment Context Alina Ko¨chling • Shirin Riazy • Marius Claus Wehner • Katharina Simbeck

Received: 28 February 2020 / Accepted: 19 October 2020 Ó The Author(s) 2020

Abstract The study aims to identify whether algorithmic decision making leads to unfair (i.e., unequal) treatment of certain protected groups in the recruitment context. Firms increasingly implement algorithmic decision making to save costs and increase efficiency. Moreover, algorithmic decision making is considered to be fairer than human decisions due to social prejudices. Recent publications, however, imply that the fairness of algorithmic decision making is not necessarily given. Therefore, to investigate this further, highly accurate algorithms were used to analyze a pre-existing data set of 10,000 video clips of individuals in self-presentation settings. The analysis shows that the under-representation concerning gender and ethnicity in the training data set leads to an unpredictable overestimation and/or underestimation of the likelihood of inviting representatives of these groups to a job interview. Furthermore, algorithms replicate the existing inequalities in the data set. Firms have to be careful when implementing algorithmic video analysis during recruitment as biases occur if the underlying training data set is unbalanced. Keywords Fairness  Bias  Artificial algorithm decision making  Recruitment  Asynchronous video interview  Ethics  HR analytics  Artificial intelligence

Accepted after two revisions by the editors of the special issue. A. Ko¨chling (&)  M. C. Wehner Heinrich-Heine-University, Du¨sseldorf, Germany e-mail: [email protected] S. Riazy  K. Simbeck HTW Berlin, Berlin, Germany

1 Introduction Currently, among recruitment functions, a global wave of enthusiasm is arising about algorithmic decision making in the context of recruitment and job interviews (Langer et al. 2019; Persson 2016). Here, algorithmic decision making can be understood as automated decision making and remote control as well as standardization of routinized decisions in the workplace (Mo¨hlmann and Zalmanson 2017). One often-used application of HR analytics in the recruiting context is algorithmic video analysis, where firms receive an evaluation of each applicant and a prediction of the applicants’ job performance. The algorithmic video analysis takes place asynchronously; the applicants record a video of themselves, which is then algorithmically evaluated (Langer et al. 2019; Dahm and Dregger 2019). Limited time and resources of recruiters simultaneously managing large pools of applicants are some of the main reasons for the rapid growth of algorithmic decision making in many companies (Leicht-Deobald et al. 2019). Algorithmic decision making in recruitment is presently well-established in large companies from a variety of industries, such as Vodafone, KPMG, BASF, and Unilever (Daugherty and Wilson 2018). It has both practic