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Facial Recognition Technology For Recruitment In The Russian Workplace

Author

Listed:
  • Maryann Osadebamwen Asemota

    (National Research University Higher School of Economics)

Abstract

Facial recognition technologies demonstrate a wide range of application fields. Among them is the use of facial recognition for recruitment. This has moved from traditional face scanning to actual emotion detection with the aim of identifying the right candidate for the respective job position. The purpose of this study was to show how facial recognition technology is applied for recruitment in Russia, as well as the benefits, risks, and challenges. The paper answers the question on how the technology has been applied in or adapted to the Russian environment as well as highlighting the corresponding benefits, risks and challenges. Russian employers usually make certain changes to use this facial recognition technology for recruitment including a reduced number of interview questions as compared to a physical interview, interpreting emotions differently and combining it with physical interviews. The benefits include the possibility of checking facial expressions in order to detect emotions, analysing emotions to get information on some personality traits, analysing candidates’ interests, creating candidates’ profiles, reactions to specific questions, checking for culture fit, and finally more objectivity. Finally, the paper argues that facial recognition technology for recruitment is still at an early developing phase in Russia. There is still a lot that can be done to ensure its proper usage for recruitment.

Suggested Citation

  • Maryann Osadebamwen Asemota, 2023. "Facial Recognition Technology For Recruitment In The Russian Workplace," HSE Working papers WP BRP 126/STI/2023, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:126sti2023
    as

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    File URL: https://wp.hse.ru/data/2023/08/17/2068455841/126STI2023.pdf
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    References listed on IDEAS

    as
    1. Tan Yigitcanlar & Kevin C. Desouza & Luke Butler & Farnoosh Roozkhosh, 2020. "Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature," Energies, MDPI, vol. 13(6), pages 1-38, March.
    2. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    3. Kuo Chi-Hsien & Shinya Nagasawa, 2019. "Applying machine learning to market analysis: Knowing your luxury consumer," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(4), pages 404-419, October.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Facial Recognition Technology; Recruitment; Artificial Intelligence; E-HRM; Automated Interviews;
    All these keywords.

    JEL classification:

    • Z - Other Special Topics

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