융합신호처리학회논문지 (Journal of the Institute of Convergence Signal Processing)

한국융합신호처리학회 (The Korea Institute of Convergence Signal Processing)
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인공지능(AI) 기반 인사관리의 윤리적·법적 영향

Ethical and Legal Implications of AI-based Human Resources Management

  • 이정우 (연세대학교 원주의과대학 인공지능빅데이터의학센터) ;
  • 이정수 (한국로봇융합연구원) ;
  • 권지훈 (건국대학교 창업지원단) ;
  • 차민이 (한국로봇융합연구원) ;
  • 김규태 (연세대학교 원주의과대학 인공지능빅데이터의학센터)
  • Jungwoo Lee (AI BigData Medical Center, Yonsei University) ;
  • Jungsoo Lee (Korea Institute of Robotics & Technology Convergence) ;
  • Ji Hun kwon (Start-up Support agency, Konkuk University) ;
  • Minyi Cha (Korea Institute of Robotics & Technology Convergence) ;
  • Kyu Tae Kim (AI BigData Medical Center, Yonsei University)
  • 투고 : 2024.06.21
  • 심사 : 2024.06.30
  • 발행 : 2024.06.30
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초록

이 연구는 인공지능(AI)을 인적 자원 관리에 활용하는 것의 윤리적 및 법적 함의, 특히 채용 과정에서 AI 인터뷰에 초점을 맞추어 조사합니다. 추론, 학습, 적응과 같은 인간 지능과 관련된 작업을 수행할 수 있는 컴퓨터 프로그램의 능력으로 정의되는 AI는 점점 더 HR 관행에 통합되고 있습니다. AI가 주도하는 인터뷰를 통해 채용에 AI를 배치하면 효율성과 객관성을 약속하지만, 동시에 중요한 윤리적 및 법적 문제도 제기됩니다. 이러한 문제에는 AI 알고리즘의 잠재적 편향, AI 의사 결정 과정의 투명성, 데이터 프라이버시 문제, 기존 노동법 및 규정 준수 등이 포함됩니다. 이 논문은 사례 연구를 분석하고 관련 문헌을 검토함으로써 이러한 과제에 대한 포괄적인 이해를 제공하고 AI 기반 HR 관행에서 윤리적 및 법적 준수를 보장하기 위한 권장 사항을 제시하는 것을 목표로 합니다. 연구 결과는 AI가 채용 효율성을 향상시킬 수 있지만, 위험을 완화하고 공정하고 투명한 채용 관행을 보장하기 위해 견고한 윤리 지침과 법적 프레임워크를 마련하는 것이 필수적임을 시사합니다.

This study investigates the ethical and legal implications of utilizing artificial intelligence (AI) in human resource management, with a particular focus on AI interviews in the recruitment process. AI, defined as the capability of computer programs to perform tasks associated with human intelligence such as reasoning, learning, and adapting, is increasingly being integrated into HR practices. The deployment of AI in recruitment, specifically through AI-driven interviews, promises efficiency and objectivity but also raises significant ethical and legal concerns. These concerns include potential biases in AI algorithms, transparency in AI decision-making processes, data privacy issues, and compliance with existing labor laws and regulations. By analyzing case studies and reviewing relevant literature, this paper aims to provide a comprehensive understanding of these challenges and propose recommendations for ensuring ethical and legal compliance in AI-based HR practices. The findings suggest that while AI can enhance recruitment efficiency, it is imperative to establish robust ethical guidelines and legal frameworks to mitigate risks and ensure fair and transparent hiring practices.

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참고문헌

  1. M. A. Boden, "AI: Its nature and future"in Oxford University Press, 2016.
  2. Y. Xu ea al., (2021 Nov.). Artificial intelligence: A powerful paradigm for scientific research, The Innovation, [Online]. 2(4), pp.1-20. Availiable: https://www.sciencedirect.com/science/article/pii/S2666675821001041?via%3Dihub 1001041?via%3Dihub
  3. H. Wang etal., (2023, Aug.). Scientific discovery in the age of artificial intelligence, Nature, [Online]. 620(7972), pp 47-60, Availiable: https://www.nature.com/articles/s41586-023-06221-2 https://doi.org/10.1038/s41586-023-06221-2
  4. I. I. Price, W. Nicholson, (2017, Nov.). Artificial intelligence in health care: Applications and legal issues, [Online]. 14(1), 7 Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3078704
  5. S. J. Schweikart, (2021, ). Who will be liable for medical malpractice in the future? How the use of artificial intelligence in medicine will shape medical tort law, Minn. JL Sci. & Tech., [Online]. 22(2), pp. 1-22. Available: https://scholarship.law.umn.edu/mjlst/vol22/iss2/2/
  6. H. Smith, K. Fotheringham, (2020, Jun.). Artificial intelligence in clinical decision-making: Rethinking liability, Medical Law International, [Online]. 20(2), pp. 131-154, 6 2020, Available: https://onlinelibrary.wiley.com/doi/full/10.1111/bioe.13222
  7. B. Boutin, (2022, Nov). State responsibility in relation to military applications of artificial intelligence, Leiden journal of international law, [Online]. 36(1), pp. 133-150. Available: https://www.cambridge.org/core/journals/leiden-journal-of-international-law/article/state-responsibility-in-relation-to-military-applications-of-artificial-intelligence/1B0454611EA1F11A8B03A5D2D052C2BE https://doi.org/10.1017/S0922156522000607
  8. M. Cummings et al., (2017, Jan.). Artificial intelligence and the future of warfare, Chatham House for the Royal Institute of International Affairs London, 2017. [Online]. Available at: https://www.chathamhouse.org/2017/01/artificial-intelligence-and-future-warfare
  9. P. Panneerselvam, "Autonomous Weapon System: Debating Legal-Ethical Consideration and Meaningful Human Control Challenges in the Military Environment", in AI, Consciousness and The New Humanism, S. Menon, S. Todariya, T. Agerwala, Singapore: Springer Nature Singapore, 2024, pp 243-258.
  10. R. Lafuente Sanchez, "Compatibility of the Future Civil Liability Regime for Artificial Intelligence with the Rules of Private International Law in Cross-Border Road Traffic Accidents Caused by Automated Vehicles", Anuario Espanol Derecho Int'l Priv., vol 22, p 139, 2022.
  11. C. Mingtsung, C. Qiaoying, W. You, "Research on the Responsibility of Automatic Driving Vehicle Accident", in 2020 4th International Seminar on Education, Management and Social Sciences (ISEMSS 2020), Atlantis Press, 2020, pp 623-630.
  12. D. C. Vladeck, (2014, Jun.). Machines without principals: liability rules and artificial intelligence, Wash. L. Rev., [Online]. 89(1), pp. 117-150. Available: https://digitalcommons.law.uw.edu/wlr/vol89/iss1/6/
  13. H.-W. Liu, C.-F. Lin, Y.-J. Chen, (2019, Feb.). Beyond State v Loomis: artificial intelligence, government algorithmization and accountability, Inter. j. of law and infor. tech., [Online]. 27(2), pp. 122-141. Available: https://academic.oup.com/ijlit/article/27/2/122/5316430?login=true https://doi.org/10.1093/ijlit/eaz001
  14. M. Mitchell, "Artificial intelligence: A guide for thinking humans." Penguin UK, 2019, ch. 1, pp. 12-24.
  15. D. Coldewey, "AI desperately needs regulation and public accountability, experts say", Techcrunch, 2018.
  16. A. L. Kotsenas, P. Balthazar, D. Andrews, J. R. Geis, T. S. Cook, (2021, Sep.). Rethinking patient consent in the era of artificial intelligence and big data, J. of the Ame. Coll. of Radio., [Online]. 18(1), pp 180-184, Available: https://www.jacr.org/article/S1546-1440(20)30965-0/fulltext https://doi.org/10.1016/j.jacr.2020.09.022
  17. S. Meyer, "Artificial intelligence and the privacy challenge", CPO Magazine, pp 1-6, 2018.
  18. M. Mylrea, N. Robinson, (2023, Oct.). Artificial Intelligence (AI) trust framework and maturity model: applying an entropy lens to improve security, privacy, and ethical AI, Entropy, [Online]. 25(10), p 1429. Available: https://www.mdpi.com/1099-4300/25/10/1429 1099-4300/25/10/1429
  19. S. Lohr, "AI and Privacy Concerns Get White House to Embrace Global Cooperation", The New York Times, p 3, 2019. https://doi.org/10.36514/UNILAW.2019..40.3
  20. Hae-won Lee, "A legal research on the artificial intelligence and tort liability", School of Law, Yonsei University, Seoul, Korea. 2021
  21. R. Rodrigues, (2020, Dec.). Legal and human rights issues of AI: Gaps, challenges and vulnerabilities, J. of Respon. Tech., [Online]. 4, pp. 100005. Available: https://www.sciencedirect.com/science/article/pii/S2666659620300056
  22. I. Taylor, (2020, Nov.). Who Is Responsible for Killer Robots? Autonomous Weapons, Group Agency, and the Military-Industrial Complex, J. Appl. Philoso., [Online]. 38(2), pp. 320-334. Available: https://onlinelibrary.wiley.com/doi/full/10.1111/japp.12469
  23. J. Beck, M. Stern, E. Haugsjaa, (1996, Sep.). Applications of AI in education, XRDS, [Online]. 3(1), pp. 11-15. Available: https://dl.acm.org/doi/10.1145/332148.332153
  24. J. Cahn, "CHATBOT: Architecture, design, & development", University of Pennsylvania School of Engineering and Applied Science Department of Computer and Information Science, 2017.
  25. A. S. Lokman, M. A. Ameedeen, "Modern Chatbot Systems: A Technical Review", in Proc. of the Future Tech. Conf. 2018, pp. 1012-1023.
  26. B. Marr, Artificial intelligence in practice: how 50 successful companies used AI and machine learning to solve problems. John Wiley & Sons, 2019.
  27. A. Polisetty, D. Chakraborty, S. G, A. K. Kar, S. Pahari, (2023, Jun.). What Determines AI Adoption in Companies? Mixed-Method Evidence, J. of Computer Info. Sys., [Online]. 64(3), pp. 370-387, Available: https://www.tandfonline.com/doi/full/10.1080/08874417.2023.2219668
  28. B. A. Shawar, E. Atwell, (2007, "Different measurement metrics to evaluate a chatbot system", in Proc. of NAACL-HLT-Dialog '07, 2007, pp 89-96.
  29. F. Tahiru, (2021, Apr.). AI in education: A systematic literature review, J. of Cases on Info. Tech., [Online]. 23(1), pp. 1-20. Available: https://www.igi-global.com/gateway/article/266434 https://doi.org/10.4018/JCIT.2021010101
  30. M. Tarafdar, C. M. Beath, J. W. Ross, "Using AI to enhance business operations", MIT Sloan Management Review, 60(4), pp 37-44. Summer 2019 Issue
  31. K. Zhang, A. B. Aslan, (2021, June). AI technologies for education: Recent research & future directions, Computers and Education: Artificial Intelligence, [Online]. 2, p 100025, Available: https://www.sciencedirect.com/science/article/pii/S2666920X21000199
  32. A. Delfanti, B. Frey, (,). Humanly Extended Automation or the Future of Work Seen through Amazon Patents, Science, Technology & Human Values, [Online]. 46(3), pp. 655-682. Available: https://journals.sagepub.com/doi/10.1177/0162243920943665
  33. N. Soni, E. K. Sharma, N. Singh, A. Kapoor, (2019, May). Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models, [Online]. Available at: https://arxiv.org/abs/1905.02092v1
  34. Liz Hilton Segel, Homayoun Hatami, "The state of AI in early 2024: Gen AI adoption spikes and starts to generate value", McKinsey & Company The Daily Read, no. 5, pp. 1-23, May 2024.
  35. N. Nawaz, "Artificial intelligence interchange human intervention in the recruitment process in Indian software industry", 2019, 접근된: 2024년 6월 15일. [Online]. Available: https://papers.ssrn.com/sol3/ papers.cfm?abstract_id=3521912
  36. B. Kapoor, J. Sherif, (2012, Oct.). Human resources in an enriched environment of business intelligence, Kybernetes, [Online]. 41(10), pp. 1625-1637. Available: https://www.emerald.com/insight/content/doi/10.1108/03684921211276792/full/html
  37. S. Semmler, Z. Rose, (2017, .). Artificial Intelligence: Application today and implications tomorrow, Duke L. & Tech. Rev., [Online]. 16, pp. 85. Available: https://scholarship.law.duke.edu/dltr/vol16/iss1/3/
  38. H. Roberts, E. Hine, M. Taddeo, L. Floridi, (2024, Mar.). Global AI governance: barriers and pathways forward, International Affairs, [Online]. 100(3), pp. 1275-1286. Available: https://papers.ssrn.com/sol3/ papers.cfm?abstract_id=4588040 https://doi.org/10.1093/ia/iiae073
  39. E. Bryndin, (2019 , Jun.). Human digital doubles with technological cognitive thinking and adaptive behaviour, Software Engineering, [Online]. 7(1), pp. 1-9. Available: https://sciencepublishinggroup.com/article/10.11648/10040173
  40. L. Baum, P. Weber, L.-M. Kolb, "The Explanation Matters: Enhancing AI Adoption in Human Resource Management", PACIS 2023 PROCEEDINGS, 2023, pp. 1151.
  41. O. Ahmed, (2018, Dec.). Artificial Intelligence in Human Resources, [Online]. 5(4), pp. 971-978 Available: https://osf.io/preprints/arabixiv/cfwvm/
  42. T. H. Davenport, R. Ronanki, "Artificial intelligence for the real world", Harvard business review, 96(1), pp. 108-116, 2018.
  43. M. M. Abdeldayem, S. H. Aldulaimi, (2020, Jan.). Trends and opportunities of artificial intelligence in human resource management: Aspirations for public sector in Bahrain, International Journal of Scientific and Technology Research, [Online]. 9(1), pp. 3867-3871. Available: https://www.researchgate.net/publication/340460650_Trends_And_Opportunities_Of_Artificial_Intelligence_In_Human_Resource_Management_Aspirations_For_Public_Sector_In_Bahrain
  44. S. Berhil, H. Benlahmar, N. Labani, (2020, Apr.). A review paper on artificial intelligence at the service of human resources management, Indonesian Journal of Electrical Engineering and Computer Science, [Online]. 18(1), pp. 32-40. Available: https://doi.org/10.11591/ijeecs.v18.i1.pp32-40
  45. J. E. Bessen, M. Goos, A. Salomons, W. Van den Berge, "Automatic reaction-what happens to workers at firms that automate?, Boston Univ. School of Law, Law and Economics Research Paper, pp. 1-96, Feb. 2019.
  46. A. A. Mahmoud, T. A. Shawabkeh, W. A. Salameh, I. Al Amro, "Performance predicting in hiring process and performance appraisals using machine learning", in 2019 10th international conference on information and communication systems (ICICS), IEEE, 2019, pp 110-115.
  47. Williams, (2018, Sep.). Does competency-based education with blockchain signal a new mission for universities?, Journal of Higher Education Policy and Management, 41(1), pp. 104-117. Available: https://www.tandfonline.com/doi/pdf/10.1080/1360080X.2018.1520491
  48. A. Radonjic, "The interaction of artificial intelligence and design thinking in the development of HR and decision-making trends", Thesis, ISCTE-Instituto Universitario de Lisboa, Portugal, 2019.
  49. P. Rastgoo, "The role of human resources competency in improving the manager performance", Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, vol 64, no. 1, pp. 341-350, 2016. https://doi.org/10.11118/actaun201664010341
  50. J. Anderson, L. Rainie, A. Luchsinger, "Artificial intelligence and the future of humans", Pew Research Center, vol 10, no. 12, 2018,
  51. F. Sakka, M. E. H. El Maknouzi, I. M. Jadalhaq, (2020, May). Supporting organisational justice through a legal framework for performance appraisal in the United Arab Emirates: Management case and comparison with the French system, E-Journal of International and Comparative Labour Studies, [Online]. 9(2), pp. 6-45 Available: https://ejcls.adapt.it/index.php/ ejcls_adapt/article/view/861
  52. P. Tambe, P. Cappelli, V. Yakubovich, (2018, Nov.). Artificial Intelligence in Human Resources Management: Challenges and a Path Forward, California Management Review, [Online]. 61(4), pp. 15-42, Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3263878 https://doi.org/10.1177/0008125618790246
  53. M. U. Scherer, "Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies", Harv. JL & Tech., vol 29, p 353, 2015.
  54. J. Vrchota, M. Marikova, P. Rehor, L. Rolinek, R. Tousek, (2019,). Human resources readiness for industry 4.0, Journal of Open Innovation: Technology, Market, and Complexity, [Online]. 6(1), pp. 3-22, Available: https://www.sciencedirect.com/science/article/pii/S2199853122002566?via%3Dihub https://doi.org/10.3390/joitmc6010003
  55. S. A. Wright, A. E. Schultz, "The rising tide of artificial intelligence and business automation: Developing an ethical framework", Business Horizons, vol 61, no. 6, pp. 823-832, 2018. https://doi.org/10.1016/j.bushor.2018.07.001
  56. D. S. Hamermesh, J. Biddle, "Beauty and the labor market". National Bureau of Economic Research Cambridge, pp. 1-46, Mass., USA, 1993
  57. B. J. Ruffle, Z. Shtudiner, (2015, Aug.). Are Good-Looking People More Employable?, Management Science, 61(8), pp. 1760-1776, https://doi.org/10.1287/mnsc.2014.1927
  58. D. Sutic, I. Breskovic, R. Huic, I. Jukic, "Automatic evaluation of facial attractiveness", in The 33rd International Convention MIPRO, IEEE, 2010, pp 1339-1342.
  59. J. Gan, L. Li, Y. Zhai, Y. Liu, (2014, Nov.). Deep self-taught learning for facial beauty prediction, Neurocomputing, [Online]. 144(20), pp 295-303. Available: https://www.sciencedirect.com/science/article/pii/S0925231214006468 https://doi.org/10.1016/j.neucom.2014.05.028
  60. Jun Kim, Major Issues in the Amendment to the Act on the Prohibition of Age Discrimination in Employment and Promotion of Employment of the Elderly, Issues and Issues, National Assembly Legislative Investigation Agency, vol. 55, 2012, pp. 1-4
  61. J. Hayashi, M. Yasumoto, H. Ito, Y. Niwa, H. Koshimizu, "Age and gender estimation from facial image processing", in Proceedings of the 41st SICE Annual Conference. SICE 2002., IEEE, 2002, pp 13-18.
  62. S. E. Padme, P. S. Desai, (2015, Dec.). Estimation of age from face images, [Online]. Int. J. Sci. Res, 4(12), pp. 1927-1931. Available: https://www.ijsr.net/archive/v4i12/NOV152411.pdf https://doi.org/10.21275/v4i12.NOV152411
  63. M. Carlsson, S. Eriksson, (2019, "Age discrimination in hiring decisions: Evidence from a field experiment in the labor market", Labour Economics, vol 59, pp 173-183, 2019. https://doi.org/10.1016/j.labeco.2019.03.002
  64. J. Buolamwini, T. Gebru, "Gender shades: Intersectional accuracy disparities in commercial gender classification", in Conference on fairness, accountability and transparency, PMLR, 2018, pp 77-91.
  65. S. Buell, "MIT researcher: artificial intelligence has a race problem and we need to fix it", The Boston Magazine, vol 24, Jan, 2019.
  66. V. Muthukumar et al., (2018, Nov.). Understanding Unequal Gender Classification Accuracy from Face Images, Computer Vision and Pattern Recognition, 1, pp. 1-11. [Online]. Available: http://arxiv.org/abs/1812.00099
  67. S. Zafeiriou, C. Zhang, Z. Zhang, (2015, Sep.). A survey on face detection in the wild: past, present and future, Computer Vision and Image Understanding, [Online]. 138, pp 1-24. Available: https://www.sciencedirect.com/science/article/pii/S1077314215000727 1077314215000727
  68. Y. Bai, Y. Zhang, M. Ding, B. Ghanem, "Finding tiny faces in the wild with generative adversarial network", in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp 21-30.
  69. J. Kuczmarski, "Reducing gender bias in Google Translate", Google blog, vol 6, 2018.
  70. A. Bastanfard, M. A. Nik, M. M. Dehshibi, "Iranian face database with age, pose and expression", in 2007 International Conference on Machine Vision, IEEE, 2007, pp 50-55.
  71. S. Leavy, "Gender bias in artificial intelligence: the need for diversity and gender theory in machine learning", in Proceedings of the 1st International Workshop on Gender Equality in Software Engineering, Gothenburg Sweden: ACM, 5 2018, pp 14-16.
  72. R. CHAPUIS, "Les impacts de l'intelligence artificielle sur l'emploi: comment favoriser la complementarite avec l'humain et faire emerger de nouveaux types de metiers?", Enjeux numeriques, vol 1, pp 37-43, 2018. https://doi.org/10.3917/ennu.001.0038
  73. V. De Stefano, (2019, Jun.). Negotiating the Algorithm': Automation, Artificial Intelligence, and Labor Protection, Comp. Lab. Law & Poli. J., [Online]. 41(1), pp. 15-46, 2019. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3178233
  74. B. Pandya, "A competency framework for virtual HR professionals in an artificial intelligence age", Mokslines Leidybos Deimantas-Diamond Scientific Publication, pp 27-48, 2019.
  75. A. E. Prince, D. Schwarcz, (2020, Mar.). Proxy discrimination in the age of artificial intelligence and big data, Iowa Law Rev., [Online]. 105(3), pp. 1257. Available: https://ilr.law.uiowa.edu/print/volume-105-issue-3/proxy-discrimination-in-the-age-of-artificial-intelligence-and-big-data 105-issue-3/proxy-discrimination-in-the-age-of-artificial-intelligence-and-big-data
  76. M. C. Tschantz, "What is Proxy Discrimination?" in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022, pp. 1993-2003.
  77. S. Barocas, A. D. Selbst, "Big data's disparate impact", Calif. L. Rev., vol 104, pp. 671, 2016.
  78. M. Smith, S. Neupane, "Artificial intelligence and human development: toward a research agenda", IDRC Research Results, pp. 1-63, 2018