Examining the use of artificial intelligence in recruitment processes
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DOI: 10.36096/brss.v2i4.234
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References listed on IDEAS
- Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
- Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
- David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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- Gagandeep Singh & Indrapriya Kularatne, 2025. "The impacts of artificial intelligence (AI) driven hiring processes on job applicants’ experience: a comparative study between New Zealand and India," SN Business & Economics, Springer, vol. 5(3), pages 1-27, March.
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