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Automatisierte Videointerviews: Künstlich intelligent, aber fair?

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Personalauswahl 4.0

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Zusammenfassung

Fortschritte im maschinellen Lernen bieten die Chance, Fairness und Genauigkeit von Auswahlverfahren zu verbessern, insbesondere von Interviews. In diesem Beitrag wird beschrieben, wie die Evaluation von Interviews automatisiert wird und welche Techniken des Maschinellen Lernens angewendet werden, um Fairness und Genauigkeit gegenüber rein menschlichen Bewertungen zu verbessern. Hierzu hat HireVue ein neuwertiges Verfahren, die Multipenalty-Optimierung, entwickelt, womit Modelle im maschinellen Lernen gleichzeitig für Fairness und Genauigkeit optimiert werden.

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Author information

Authors and Affiliations

  1. HireVue, London, Großbritannien

    Franziska Leutner

  2. HireVue, South Jordan, USA

    Nathan Mondragon

Authors
  1. Franziska Leutner
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  2. Nathan Mondragon
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Correspondence to Franziska Leutner .

Editor information

Editors and Affiliations

  1. Köln, Nordrhein-Westfalen, Deutschland

    Klaus P. Stulle

  2. Hamburg, Hamburg, Deutschland

    Richard T. Justenhoven

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© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

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Leutner, F., Mondragon, N. (2023). Automatisierte Videointerviews: Künstlich intelligent, aber fair?. In: Stulle, K.P., Justenhoven, R.T. (eds) Personalauswahl 4.0. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-42142-7_9

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  • DOI: https://doi.org/10.1007/978-3-658-42142-7_9

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  • Publisher Name: Springer Gabler, Wiesbaden

  • Print ISBN: 978-3-658-42141-0

  • Online ISBN: 978-3-658-42142-7

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