IDEAS home Printed from https://ideas.repec.org/p/hig/wpaper/126sti2023.html
   My bibliography  Save this paper

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

    Download full text from publisher

    File URL: https://wp.hse.ru/data/2023/08/17/2068455841/126STI2023.pdf
    Download Restriction: no
    ---><---

    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christina Kakderi & Eleni Oikonomaki & Ilektra Papadaki, 2021. "Smart and Resilient Urban Futures for Sustainability in the Post COVID-19 Era: A Review of Policy Responses on Urban Mobility," Sustainability, MDPI, vol. 13(11), pages 1-21, June.
    2. Yu Cao & Cong Xu & Syahrul Nizam Kamaruzzaman & Nur Mardhiyah Aziz, 2022. "A Systematic Review of Green Building Development in China: Advantages, Challenges and Future Directions," Sustainability, MDPI, vol. 14(19), pages 1-29, September.
    3. Palmyra Repette & Jamile Sabatini-Marques & Tan Yigitcanlar & Denilson Sell & Eduardo Costa, 2021. "The Evolution of City-as-a-Platform: Smart Urban Development Governance with Collective Knowledge-Based Platform Urbanism," Land, MDPI, vol. 10(1), pages 1-25, January.
    4. 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.
    5. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.
    6. Elisabeth A. Shrimpton & Dexter Hunt & Chris D.F. Rogers, 2021. "Justice in (English) Water Infrastructure: A Systematic Review," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    7. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    8. Antonio De Nicola & Maria Luisa Villani, 2021. "Smart City Ontologies and Their Applications: A Systematic Literature Review," Sustainability, MDPI, vol. 13(10), pages 1-40, May.
    9. Hong Jiang & Jinlong Gai & Shukuan Zhao & Peggy E. Chaudhry & Sohail S. Chaudhry, 2022. "Applications and development of artificial intelligence system from the perspective of system science: A bibliometric review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 361-378, May.
    10. Hanna Obracht-Prondzyńska & Ewa Duda & Helena Anacka & Jolanta Kowal, 2022. "Greencoin as an AI-Based Solution Shaping Climate Awareness," IJERPH, MDPI, vol. 19(18), pages 1-25, September.
    11. JinHyo Joseph Yun & Xiaofei Zhao & KwangHo Jung & Tan Yigitcanlar, 2020. "The Culture for Open Innovation Dynamics," Sustainability, MDPI, vol. 12(12), pages 1-21, June.
    12. Seng Boon Lim & Jalaluddin Abdul Malek & Md Farabi Yussoff Md Yussoff & Tan Yigitcanlar, 2021. "Understanding and Acceptance of Smart City Policies: Practitioners’ Perspectives on the Malaysian Smart City Framework," Sustainability, MDPI, vol. 13(17), pages 1-31, August.
    13. Catarina N. S. Silva & Justas Dainys & Sean Simmons & Vincentas Vienožinskis & Asta Audzijonyte, 2022. "A Scalable Open-Source Framework for Machine Learning-Based Image Collection, Annotation and Classification: A Case Study for Automatic Fish Species Identification," Sustainability, MDPI, vol. 14(21), pages 1-13, November.
    14. Wu, Min & Wang, Nanxi & Yuen, Kum Fai, 2023. "Can autonomy level and anthropomorphic characteristics affect public acceptance and trust towards shared autonomous vehicles?," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    15. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    16. Mohammed Balfaqih & Soltan Abed Alharbi, 2022. "Associated Information and Communication Technologies Challenges of Smart City Development," Sustainability, MDPI, vol. 14(23), pages 1-27, December.
    17. Yu Cao & Liyan Huang & Nur Mardhiyah Aziz & Syahrul Nizam Kamaruzzaman, 2022. "Building Information Modelling (BIM) Capabilities in the Design and Planning of Rural Settlements in China: A Systematic Review," Land, MDPI, vol. 11(10), pages 1-34, October.
    18. David Mhlanga, 2021. "Artificial Intelligence in the Industry 4.0, and Its Impact on Poverty, Innovation, Infrastructure Development, and the Sustainable Development Goals: Lessons from Emerging Economies?," Sustainability, MDPI, vol. 13(11), pages 1-16, May.
    19. Torabi, Zabih-Allah & Rezvani, Mohammad Reza & Hall, C. Michael & Allam, Zaheer, 2023. "On the post-pandemic travel boom: How capacity building and smart tourism technologies in rural areas can help - evidence from Iran," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    20. Zulamir Hassani, Afdhal & Yusoff, Fazirah & Wan Zain, Wan Nor Aisyah, 2021. "Fair and Responsible in Logistics IR 4.0," MPRA Paper 108432, University Library of Munich, Germany.

    More about this item

    Keywords

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

    JEL classification:

    • Z - Other Special Topics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hig:wpaper:126sti2023. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Shamil Abdulaev or Shamil Abdulaev (email available below). General contact details of provider: https://edirc.repec.org/data/hsecoru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.