TY - JOUR A1 - Köchling, Alina A1 - Riazy, Shirin A1 - Wehner, Marius Claus A1 - Simbeck, Katharina T1 - Highly Accurate, But Still Discriminatory JF - Business & Information Systems Engineering N2 - 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. KW - Künstliche Intelligenz KW - Fairness KW - Bias KW - Artificial algorithm decision making KW - Recruitment KW - Asynchronous video interview KW - Ethics KW - HR analytics KW - Artificial intelligence KW - Bias KW - Ethik KW - Personalbeschaffung KW - Personalorganisation Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:523-17115 SN - 2363-7005 SN - 1867-0202 VL - 63 IS - 1 SP - 39 EP - 54 PB - Springer Fachmedien Wiesbaden ER -