Highly accurate, but still discriminatory. A fairness evaluation of algorithmic video analysis in the recruitment context.

Autor/inn/en	Köchling, Alina; Riazy, Shirin; Wehner, Marius Claus; Simbeck, Katharina
Titel	Highly accurate, but still discriminatory. A fairness evaluation of algorithmic video analysis in the recruitment context.
Quelle	In: Business & information systems engineering, 63 (2021) 1, S. 39-54 PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Beigaben	Literaturangaben; Abbildungen; Tabellen
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	1867-0202; 2363-7005
DOI	10.1007/s12599-020-00673-w
Schlagwörter	Entscheidung; Entscheidungshilfe; Algorithmus; Diskriminierung; Entscheidung; Ungerechtigkeit; Datenanalyse; Datenauswertung; Ungleichheit; Videoaufzeichnung; Videoanalyse; Videoaufzeichnung; Algorithmus; Datenanalyse; Datenauswertung; Ungerechtigkeit; Ungleichheit; Diskriminierung; Analyseverfahren
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. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2023/1