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Original Article
Mock Interviewer
Dr. D Kirubha1
Anneshan Choudhury2
Prakhar Srivastava3
Prashant Kumar Singh4
1 HOD, Department of CSE, Raja Rajeswari college of Engineering, Bengaluru, Karnataka, India. 2 3 4 Department of CSE, RajaRajeswari college of Engineering, Bengaluru, Karnataka, India
Published Online: January-February 2026
Pages: 59-64
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260701007
References
1. F. Chamorro-Premuzic, T. H. Davenport, and J. Bersin,“AI in talent acquisition,” MIT Sloan Management Review,vol. 60, no. 2, pp.
1–9, 2019.
2. A. K. Upadhyay and K. Khandelwal,“Applying artificial intelligence: Implications for recruitment,”Strategic HR Review, vol. 17, no.
5, pp. 255–258, 2018.
3. I. Naim, Y. G. Yao, and B. G. Patra,“Automated analysis of interview videos using multimodal cues,” IEEE Transactions on Affective
Computing, vol. 9, no. 4, pp. 1–13, 2018.
4. L. Chen, Z. Li, and H. Zhang, “Deep learning-based video interview assessment,” IEEE Access, vol. 10, pp. 112345–112357, 2022.
5. T. Baltrusaitis, C. Ahuja, and L. P. Morency, “Multimodal machine learning: A survey,” IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 41, no. 2, pp. 423–443, 2019.
6. Y. Zhang, S. Wang, and J. Liu, “Multimodal fusion for automated interview evaluation,” Pattern Recognition Letters, vol. 138, pp.
508–514, 2021.
7. K. Kenthapadi, D. A. Bennett, and E. K. Linder,“Fairness in algorithmic hiring,” arXiv preprint arXiv:1706.08976, 2017.
8. R. Singh and M. Bhatia, “Resume screening using machine learning,” International Journal of Computer Applications, vol. 174, no.
20, 2021.
9. M. Raghavan, S. Barocas, J. Kleinberg, and K. Levy, “Mitigating bias in algorithmic hiring,” Proceedings of the ACM FAT
Conference, pp. 469–481, 2020.
10. M. Köchling and M. C. Wehner, “Discriminated by an algorithm,” Business Research, vol. 13, pp. 795–848, 2020.
11. C. Molnar, Interpretable Machine Learning, 2nd ed., Leanpub, 2022.
1–9, 2019.
2. A. K. Upadhyay and K. Khandelwal,“Applying artificial intelligence: Implications for recruitment,”Strategic HR Review, vol. 17, no.
5, pp. 255–258, 2018.
3. I. Naim, Y. G. Yao, and B. G. Patra,“Automated analysis of interview videos using multimodal cues,” IEEE Transactions on Affective
Computing, vol. 9, no. 4, pp. 1–13, 2018.
4. L. Chen, Z. Li, and H. Zhang, “Deep learning-based video interview assessment,” IEEE Access, vol. 10, pp. 112345–112357, 2022.
5. T. Baltrusaitis, C. Ahuja, and L. P. Morency, “Multimodal machine learning: A survey,” IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 41, no. 2, pp. 423–443, 2019.
6. Y. Zhang, S. Wang, and J. Liu, “Multimodal fusion for automated interview evaluation,” Pattern Recognition Letters, vol. 138, pp.
508–514, 2021.
7. K. Kenthapadi, D. A. Bennett, and E. K. Linder,“Fairness in algorithmic hiring,” arXiv preprint arXiv:1706.08976, 2017.
8. R. Singh and M. Bhatia, “Resume screening using machine learning,” International Journal of Computer Applications, vol. 174, no.
20, 2021.
9. M. Raghavan, S. Barocas, J. Kleinberg, and K. Levy, “Mitigating bias in algorithmic hiring,” Proceedings of the ACM FAT
Conference, pp. 469–481, 2020.
10. M. Köchling and M. C. Wehner, “Discriminated by an algorithm,” Business Research, vol. 13, pp. 795–848, 2020.
11. C. Molnar, Interpretable Machine Learning, 2nd ed., Leanpub, 2022.
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