Evans Uhunoma

7% of people of colour in the UK are unemployed as compared to 4% for whites (UK Gov, 2021). While recent breakthroughs in face recognition technology (FRT) have enhanced the process of recruitment through the use of on-demand- asynchronous video interview solutions (Tambe, et al., 2019; Harwell, 2019; Nawaz, 2020), several other studies have however confirmed that FRT and its application in recruitment activities are biased towards people of colour (Izario et al., 2017; Buolamwini, 2019; Simonite, 2019). Consequently, this is predicted to lead to an increased diversity crisis within the workplace as the employment of people of colour may even lessen further (West et al., 2019). The aim of this dissertation was therefore to understand how on-demand-asynchronous video interview solutions (ODAVIS) capture peculiarities in faces. Specifically, how creators of FRT determine candidates’ suitability for jobs using FRT’s facial analysis, if the data used to train their model is representative of the faces in the world and what reasonable adjustments (if any) are made for people of colour. The major findings include the use of facial landmarks and gaze estimators to analyse candidates’ facial expressions and emotions during interviews, the use of non-representative training data that leads to inconsistency in performance, and the inclusion of certain adjustments (like automatic flashlights, data augmentation or bias audit) during the designs of the solutions and implementation, to close the gaps of inconsistent performance observed for different races.

Facial Recognition Technology (FRT), On-Demand-Asynchronous Video Interview Solutions (ODAVIS), People of Colour (POC).

1) Acharya, A.S., Prakash, A., Saxena, P. and Nigam, A. 2013. Sampling: Why and how of it. Indian Journal of Medical Specialties. 4(2), pp.330-333.

2) ACLU, 2020. Wrongfully Arrested Because Face Recognition Can’t Tell Black People Apart [Online]. [Accessed 23 May 2022]. Available from: https://www.aclu.org/news/privacy-technology/wrongfully-arrested-because-face-recognition- cant-tell-black-people-apart/

3) Ahmed, O. 2018. Artificial intelligence in HR. International Journal of Research and Analytical Reviews. 5(4), pp.971-978.

4) AI Stack Exchanges. 2019. How long it takes to train face recognition deep neural network? [Online]. [Accessed 18 July 2022]. Available from: https://ai.stackexchange.com/questions/12513/how-long-it-takes-to-train-face-recognition- deep-neural-network-rough-estimati

5) Barragan-Jason, G., Cauchoix, M. and Barbeau, E.J. 2015. The neural speed of familiar face recognition. Neuropsychologia. 75, pp.390-401.

6) BBC, 2022. Job hunting for neurodivergent people: 'AI recruitment means I’ve got zero chance' [Online]. [Accessed 23 May 2022]. Available from:
https://www.bbc.co.uk/bbcthree/article/c62bcab6-db6f-4026-90bb-7f508705a65b

7) Bell, D. 1973. The Coming of Post-Industrial Society. New York: Basic Books.

8) Black, J.S. and Esch, V.P., 2020. AI-enabled recruiting: What is it and how should a manager use it? Business Horizons. 63(2), pp.215-226.

9) Braun, V. and Clarke, V. 2012. Thematic analysis. APA Handbook of Research Methods in Psychology. 2, pp.57-71.

10) Bryman A. and Bell, E. 2015. Business Research Methods. 4 th ed. Oxford: Oxford University Press.

11) Brynjolfsson, E. and McAfee, A. 2014. The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York: WW Norton & Company.

12) Buolamwini, J. 2019. Response: Racial and Gender bias in Amazon Rekognition — Commercial AI System for Analyzing Faces. [Online]. [Accessed 09 May 2022]. Available from:
https://medium.com/@Joy.Buolamwini/response-racial-and-gender-bias-in-amazon-rekognition-commercial-ai-system-for-analyzing-faces-a289222eeced

13) Buolamwini, J. and Gebru, T. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency. pp. 77-91.

14) Byrne, D. 1971. The attraction paradigm. New York: Academic Press.

15) Capterra. 2022. Video Interview Software for the United Kingdom? [Online]. [Accessed 27 May 2022]. Available from: https://www.capterra.co.uk/directory/30821/video-interviewing/software

16) Choudhury, A. 2020. 10 Face Datasets to Start Facial Recognition Projects. [Online]. [Accessed 29 May 2022]. Available from: https://analyticsindiamag.com/10-face-datasets-to-start-facial-recognition-projects/

17) Chowdhury, M. F. 2014. Interpretivism in aiding our understanding of the contemporary social world. Open Journal of Philosophy. 4(3). pp.327-347.

18) Choy, L.T. 2014. The strengths and weaknesses of research methodology: Comparison and complimentary between qualitative and quantitative approaches. IOSR journal of humanities and social science. 19(4), pp.99-104.

19) Cohen, L., Manion, L. and Morrison, K. 2002. Research methods in education. London: Routledge.

20) Crawford, K. 2016. Artificial Intelligence’s White Guy Problem. The New York Times. [Online]. [Accessed 09 May 2022]. Available from:
https://www.nytimes.com/2016/06/26/opinion/sunday/artificial-intelligences-white-guy-problem.html

21) De Brito, A.C.V., Mascarenhas, A.P.F.M., de Souza, J.R., de Campos, J.A.P., Simões, M.A.C. and da Silva, R.M. 2021. Face recognition in Service robotics: Analysis of the padding effect according to people age Reconhecimento facial em robótica de serviço: análise do efeito de preenchimento de acordo com a idade das pessoas. Brazilian Journal of Development, 7(12). pp.115849-115865.

22) Deloitte. 2021. Challenges of fairness in AI decisions [Online]. [Accessed 04 January 2022]. Available from: https://ukfinancialservicesinsights.deloitte.com/post/102ftmq/challenges-of-fairness-in-ai-decisions/

23) Doug, R. 2018. Artificial Intelligence for Business: What you need to know about Machine Learning and Neural Networks, 2018. Chicago Lakeshore Press: Beaverton.

24) Duchaine, B. and Nakayama, K. 2006. The Cambridge Face Memory Test: results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants. Neuropsychologia. 44, pp.576– 585.

25) Easterby-Smith, M., Jaspersen, L.J., Thorpe, R. and Valizade, D. 2021. Management and business research. Newcastle: Sage.

26) Eisenhardt, K.M., Graebner, M.E. and Sonenshein, S., 2016. Grand challenges and inductive methods: Rigor without rigor mortis. Academy of management journal. 59(4), pp.1113-1123.

27) Equality Act. 2010. Statutory Code of Practice Employment. Equality and Human Rights Commission.

28) Etikan, I. and Bala, K. 2017. Sampling and sampling methods. Biometrics and Biostatistics International Journal. 5(6), p.00149.

29) Faux, F. and Luthon, F. 2012. Theory of evidence for face detection and tracking. International Journal of Approximate Reasoning. 53(5), pp.728-746.

30) Forbes. 2022. Five Ways the Future Of Facial Recognition Will Become More Prevalent In Business And Life In 2022 [Online]. [Accessed 27 May 2022]. Available from:
https://www.forbes.com/sites/forbestechcouncil/2022/05/03/five-ways-the-future-of-facial-recognition-will-become-more-prevalent-in-business-and-life-in-2022

31) Francis, J.J., Johnston, M., Robertson, C., Glidewell, L., Entwhistle, V., Eccles, M.P., and Grimshaw, J.M. 2010. What is an adequate sample size? Operationalising data saturation for theory-driven interview studies. Psychology and Health. 25, pp.1229–1245.

32) Galterio, M.G., Shavit, S.A. and Hayajneh, T. 2018. A review of facial biometrics security for smart devices. Computers. 7(3), p.37.

33) Gill, J. and Johnson, P. 2002. Research methods for managers. Newcastle: Sage.

34) Guo, Y., Zhang, L., Hu, Y., He, X. and Gao, J. 2016, October. Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In European conference on computer vision. pp.87-102. Copenhagen: Springer Cham.

35) H2B. 2019. NHS Graduate Scheme Video Interview: Secure Your NHS Career! [Online]. [Accessed 28 May 2022]. Available from: https://www.how2become.com/blog/nhs-graduate-scheme-video-interview

36) Hammersley, M. 2013. The myth of research-based policy and practice. Newcastle: Sage.

37) Han, H. and Jain, A.K. 2014. Age, Gender and Race Estimation from Unconstrained Face Images. MSU Technical Report. [Online]. [Accessed 30 May 2022]. Available from:
http://biometrics.cse.msu.edu/Publications/Face/HanJain_UnconstrainedAgeGenderRaceEstimation_MSUTechReport2 014.pdf.

38) Harwell, D. 2019. A face-scanning algorithm increasingly decides whether you deserve the job [Online]. [Accessed 15 July 2022]. Available from: https://www.washingtonpost.com/technology/2019/10/22/ai-hiring-face-scanning-algorithm- increasingly-decides-whether-you-deserve-job/

39) Iaydjiev, I. 2013. A pragmatic approach to social science. E-International Relations Students. [Online]. [Accessed 03 June 2022]. Available from: https://www.e-ir.info/2013/03/01/a-pragmatic-approach-to-social-science/

40) Izario, D., Izario, B., Castro, D. and Iano, Y. 2017. Face Recognition Techniques Using Artificial Intelligence for Audio-Visual Animations. SET International Journal of Broadcast Engineering. 2017(3), pp.78-82.

41) Jia, M. 2017. Statistical Learning Methods for Facial Recognition [Online]. [Accessed 17 July 2022]. Available from: https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=2075&context=art_sci_etds

42) Jo, S. 2018. Volvo Uses Car’s A.I. To Conduct Job Interviews [Online]. [Accessed 18 July 2022]. Available from:
https://autoconversion.net/blog/automotive/news-brief-volvo-uses-cars-a-i-to-conduct-job-interviews/

43) Kahn, J. and Bass, D. 2017. Black AI Workshop Becomes Latest Flashpoint in Tech’s Culture War. Bloomberg. [Online]. [Accessed 30 May 2022]. Available from: https://www.bloomberg.com/news/articles/2017-10-20/black-ai-workshop- becomes-latest-flashpoint-in-tech-s-culture-war/

44) Kanade, T. 1973. Picture processing system by computer complex and recognition of human faces. Doctoral dissertation. Kyoto: Dept. of Information Science, Kyoto Univ.

45) Kang, H.R., Yang, H.D. and Rowley, C. 2006. Factors in team effectiveness: cognitive and demographic similarities of software development team members. Human Relations. 59, pp.1681–1710.

46) Kaur, P., Krishan, K., Sharma, S.K. and Kanchan, T. 2020. Facial-recognition algorithms: A literature review. Medicine, Science and the Law. 60(2), pp.131-139.

47) Klein, H.K. and Myers, M.D. 1999 A set of principles for conducting and evaluating interpretive field studies in information systems. MIS Quarterly. 23, pp.67–93.

48) Kleinman, Z. 2019. Amazon: Facial recognition bias claims are 'misleading' [Online]. [Accessed 29 May 2022]. Available from: https://www.bbc.co.uk/news/technology-47117299

49) Kumra, S. and Manfredi, S. 2012. Managing Equality and Diversity: Theory and Practice. Oxford: Oxford University Press.

50) Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett. 2016. Advances in Neural Information Processing Systems. Curran Associates. 29, pp.3315–3323.

51) Loconsole, C., Miranda, C.R., Augusto, G., Frisoli, A., and Orvalho, V. 2014. Real-time emotion recognition novel method for geometrical facial features extraction. Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP). pp. 378-385.

52) Lohia, P.K., Ramamurthy, K.N., Bhide, M., Saha, D., Varshney, K.R. and Puri, R. 2019. Bias mitigation post-processing for individual and group fairness. In Icassp 2019-2019 ieee international conference on acoustics, speech and signal processing (icassp). pp. 2847-2851). IEEE.

53) Macfarlane, B. 2010. Researching with integrity: The ethics of academic enquiry. London: Routledge.

54) Masud, M., Muhammad, G., Alhumyani, H., Alshamrani, S. S., Cheikhrouhou, O., Ibrahim, S., & Hossain, M. S. 2020. Deep Learning-Based Intelligent Face Recognition in IoT-Cloud Environment. Computer Communications. 152, pp.215-222.

55) Meissner, P. and Wulf, T. 2017. The effect of cognitive diversity on the illusion of control bias in strategic decisions: An experimental investigation. European Management Journal. 35(4), pp.430-439.

56) Merler, M., Ratha, N., Feris, R.S. and Smith, J.R. 2019. [Pre-Print]. Diversity in faces. IBM AI Research. New York: J. Watson Research Centre.

57) Michael, S. 2008. The promise of appreciative inquiry as aninterview tool for field research. Development in Practice. 15(2), pp. 222-230.

58) Miller, C. 1990. Cognitive diversity within management teams: Implications for strategic decision processes and organizational performance. Texas: Graduate School of Business, University of Texas.

59) Miller, C., Burke, L., & Glick, W. 1998. Cognitive diversity among upper echelon executives: implications for strategic decision processes. Strategic Management Journal. 19, pp.39-58.

60) Myers, M.D. 2019. Qualitative research in business and management. Newcastle: Sage.

61) Nawaz, N. 2020. Artificial intelligence applications for face recognition in recruitment process. Journal of Management Information and Decision Sciences. 23, pp.499-509.

62) Ness, I. 2021. Mind the Gap: Creative Knowledge Processes Within Interdisciplinary Groups in Organizations and Higher Education. Creativity and Learning. 9, pp.195-217.

63) New York Times. 2018. Facial Recognition Is Accurate, if You’re a White Guy - Lohr, S [Online]. [Accessed 26 May 2022]. Available from: https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html

64) Nguyen, B.T., Trinh, M.H., Phan, T.V. and Nguyen, H.D. 2017, April. An efficient real-time emotion detection using camera and facial landmarks. In 2017 seventh international conference on information science and technology (ICIST). pp. 251- 255

65) Nguyen, L.S., Frauendorfer, D., Mast, M.S. and Gatica-Perez, D. 2014. Hire me: Computational inference of hirability in employment interviews based on nonverbal behavior. IEEE transactions on multimedia, 16(4). pp.1018-1031.

66) Noon, M. 2010. The shackled runner: Time to rethink positive discrimination? Work, Employment and Society. 24(4), pp.728–739.

67) Ortiz, E.G. and Becker, B.C. 2014. Face recognition for web-scale datasets. Computer Vision and Image Understanding. 118, pp.153-170.

68) Parayitam, S., and Papenhausen, C. 2016. Agreement-seeking behavior, trust, and cognitive diversity in strategic decision- making teams: Process conflict as a moderator. Journal of Advances in Management Research. 13(3), pp.292-315.

69) Parkhi, O.M., Vedaldi, A. and Zisserman, A., 2015. Deep face recognition. Oxford: University of Oxford.

70) Patton, M.Q. 2014. Qualitative research and evaluation methods: Integrating theory and practice. Newcastle: Sage publications.

71) RecFaces. 2021. What Is AI Facial Recognition Tech and How does It Work? [Online]. [Accessed 27 May 2022]. Available from: https://recfaces.com/articles/ai-facial-recognition

72) Recruiter. 2019. The rise of the Robo-recruiter [Online]. [Accessed 26 May 2022]. Available from: https://recruiter.co.uk/2019/12/rise-robo-recruiter

73) Robson, C. 2002. Real World Research. Oxford: Blackwell.

74) Sabel, J.M. 2018. One-Way, On-Demand and Self-Paced Interview Guide [Online]. [Accessed 28 May 2022]. Available from: https://www.hirevue.com/blog/hiring/video-interviewing-guide

75) Sardar, S., Tewari, G. and Babu, K.A. 2011, November. A hardware/software co-design model for face recognition using cognimem neural network chip. In 2011 International Conference on Image Information Processing. pp. 1-6).

76) Saunders, M., Lewis, P. and Thornhill, A. 2007. Research methods for business students. 4th ed. London: Pearson.

77) Saunders, M., Lewis, P. and Thornhill, A. 2012. Research methods for business students. 6th ed. Harlow: Pearson Education Limited.

78) Saunders, M., Lewis, P. and Thornhill, A. 2021. Research methods for business students. 8th ed. London: Pearson.

79) Shead, S. 2019. Google A.I. Panel Member Says Google ‘Pulled The Plug Rather Than Defend Themselves’. Forbes. [Online]. [Accessed 30 May 2022]. Available from:
https://www.forbes.com/sites/samshead/2019/04/05/google-a-i- panel-member-says-google-pulled-the-plug-rather-than-defendthemselves/#27d051439554

80) Shell. 2022. 7 tips to prepare students & graduates for an online video interview? [Online]. [Accessed 28 May 2022]. Available from: https://www.shell.com/careers/candidate-resource-centre/seven-tips-to-prepare-you-for-an-online- interview.html

81) Simonite, T. 2019. The Best Algorithms Struggle to Recognize Black Faces Equally. [Online]. [Accessed 09 May 2022]. Available from:
https://www.wired.com/story/best-algorithms-struggle-recognize-black-faces-equally/

82) Statista. 2022. Poverty rate in the United States in 2020, by ethnic group [Online]. [Accessed 19 July 2022]. Available from: https://www.statista.com/statistics/200476/us-poverty-rate-by-ethnic-group/

83) Tambe, P., Cappelli, P. and Yakubovich, V. 2019. Artificial intelligence in human resources management: Challenges and a path forward. California Management Review. 61(4), pp.15-42.

84) Tatli, A. and Özbilgin, M.F. 2012. An emic approach to intersectional study of diversity at work: A Bourdieuan framing. International Journal of Management Reviews. 14(2), pp.180-200.

85) Taylor, S.J., Bogdan, R. and DeVault, M. 2015. Introduction to qualitative research methods: A guidebook and resource. John Wiley and Sons.

86) The Guardian. 2018. Psychologists' face off reveals humans can recognise 5,000 people. [Online]. [Accessed 29 May 2022]. Available from: https://www.theguardian.com/science/2018/oct/10/how-many-faces-average-person- recognises-5000

87) Thomas, J.R., Martin, P., Etnier, J. and Silverman, S.J. 2022. Research methods in physical activity. Oxford: Human kinetics.

88) UK Gov. 2021. Unemployment: Main facts and figures [Online]. [Accessed 22 June 2022]. Available from: https://www.ethnicity-facts-figures.service.gov.uk/work-pay-and-benefits/unemployment-and-economic- inactivity/unemployment/latest

89) United Nations. 2020. Bias, racism and lies facing up to the unwanted consequences of AI [Online]. [Accessed 05 January 2022]. Available from: https://news.un.org/en/story/2020/12/1080192/

90) Weichselbaumer, D. 2016. Discrimination against female migrants wearing headscarves. Institute of Labor Economics. Bonn: IZA Discussion Paper.

91) West, S.M., Whittaker, M. and Crawford, K., 2019. Discriminating systems. AI Now. [Online]. [Accessed 09 May 2022]. Available from:
https://cdn.vox-cdn.com/uploads/chorus_asset/file/16125391/discriminating_systems_041519_2.pdf

92) Wilkinson, M. 2021. The 6 Different Types of Interviews [Online]. [Accessed 04 January 2022]. Available from: https://www.coburgbanks.co.uk/blog/assessing-applicants/6-different-types-of-interview/

93) Williams, B.A. 2017. Tech’s Troubling New Trend: Diversity Is in Your Head. The New York Times. [Online]. [Accessed 09 May 2022]. Available from: https://www.nytimes.com/2017/10/16/opinion/diversity-tech-women-silicon-valley.html

94) Wilmer, J. B., Germine, L., Chabris, C. F., Chatterjee, G., and Williams, M., Loken, E. 2010. Human face recognition ability is specific and highly heritable. Proc. Nat. Acad. Sci (PNAS). 107(11), pp.5238–5241.

95) Younis, R. 2019. Cognitive diversity and creativity: the moderating effect of collaborative climate. International Journal of Business and Management. 14(1), pp.159-168.

96) Zadeh, S. 2018. Are there universal expressions of emotion? [Online]. [Accessed 29 May 2022]. Available from: YouTube https://www.youtube.com/watch?v=-hr58Yu0yDs 97) Zhang, X., Fang, Z., Wen, Y., Li, Z. and Qiao, Y., 2017. Range loss for deep face recognition with long-tailed training data. In Proceedings of the IEEE International Conference on Computer Vision. pp. 5409-5418.

98) Zhao, J., Wang, T., Yatskar, M., Ordonez, V. and Chang, K.W. 2017. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint arXiv:1707.09457.

VOLUME 06 ISSUE 07 JULY 2023

There is an Open Access article, distributed under the term of the Creative Commons Attribution – Non Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting and building upon the work for non-commercial use, provided the original work is properly cited.