طراحی چارچوب مفهومی سنجش تحقق هوش مصنوعی مسئولیت‌پذیر در مدیریت منابع انسانی

نوع مقاله : پژوهشی کیفی

نویسندگان
1 دانشیار، گروه مدیریت، دانشکده مدیریت و حسابداری، دانشگاه حضرت معصومه (س)، قم، ایران.
2 استادیار، گروه مدیریت، دانشکده مدیریت و حسابداری، دانشگاه حضرت معصومه (س)، قم، ایران.
10.48311/ormr.2025.27577
چکیده
 فناوری هوش مصنوعی با ایجاد تحول بنیادین در مدیریت منابع انسانی، به ابزاری راهبردی در تصمیم‌گیری‌های کلیدی از جمله استخدام و ارتقا تبدیل شده است. با‌این‌حال، گزارش‌های بسیاری از تخلفات و آسیب‌های ناشی از تصمیم‌گیری خودکار حکایت دارند. براین‌اساس، هدف اصلی این پژوهش، طراحی چارچوب مفهومی سنجش تحقق هوش مصنوعی مسئولیت‌پذیر در حوزه مدیریت منابع انسانی است. در راستای تحقق این هدف، از رویکردی کیفی بهره گرفته شده است. در گام نخست، با مرور نظام‌مند ادبیات نظری و تجربی مرتبط، چارچوب مفهومی اولیه استخراج شد. سپس، به‌منظور تعمیق و غنای آن، مصاحبه‌های نیمه‌ساختار یافته‌ای با ۱۰ تن از خبرگان حوزه‌های منابع انسانی و هوش مصنوعی انجام شد. داده‌های گردآوری‌شده با روش تحلیل محتوای کیفی بررسی و تفسیر شد تا ابعاد مفهومی هوش مصنوعی مسئولیت‌پذیر به شکلی دقیق و مبتنی بر شواهد استخراج شود. یافته‌ها نشان داد که ابعاد هوش مصنوعی مسئولیت‌پذیر در شش حوزه اصلی شامل اخلاق با 7 شاخص، قابلیت اطمینان و اعتمادپذیری با 5 شاخص، پاسخ‌گویی و مسئولیت‌پذیری با 6 شاخص، شفافیت با 7 شاخص، عدالت و عدم تبعیض با 7 شاخص و همچنین امنیت و حریم خصوصی با 8 شاخص تعریف می‌شوند که با مجموعه‌ای از ۴۰ شاخص مشخص شده‌اند. این چارچوب می‌تواند مدیران منابع انسانی را در طراحی و پیاده‌سازی سیستم‌های هوش مصنوعی کمک کند تا نه‌تنها این فناوری را به شکلی مسئولانه و منطبق بر ارزش‌های انسانی و سازمانی به‌کار ببرند، بلکه با درک و به‌کارگیری هم‌زمان شش بعد کلیدی مسئولیت‌پذیری از پیامدهای ناخواسته جلوگیری کنند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Designing a Conceptual Framework for Assessing the Realization of Responsible Artificial Intelligence in Human Resource Management

نویسندگان English

mona Jamipour 1
shahnaz akbari Emami 2
1 Associate Professor, Department of Management, Faculty of Management and Accounting, Hazrat-e Masoumeh University, Qom, Iran.
2 Assistant Professor, Department of Management, Faculty of Management and Accounting, Hazrat-e Masoumeh University, Qom, Iran.
چکیده English

Artificial intelligence (AI) technologies have significantly transformed human resource management (HRM), playing a pivotal role in key decisions such as hiring and termination. However, numerous reports have highlighted risks and violations associated with automated decision-making. Accordingly, the primary aim of this study is to develop a conceptual framework for measuring the realization of responsible AI in the domain of HRM. A qualitative research approach was adopted. First, a systematic review of relevant theoretical and empirical literature was conducted to extract an initial conceptual framework. To enrich and validate this framework, semi-structured interviews were carried out with ten experts in the fields of AI and HRM. The collected data were analyzed using qualitative content analysis to identify the core dimensions of responsible AI in a rigorous, evidence-based manner. Findings revealed that responsible AI in HRM comprises six main dimensions: ethics (7 items), reliability and trustworthiness (5 items), accountability and responsibility (6 items), transparency (7 items), fairness and non-discrimination (7 items), and data privacy and security (8 items), totaling 40 specific indicators. The proposed framework provides HR managers with a practical guide for designing and implementing AI systems in a way that is not only aligned with human and organizational values but also safeguards against unintended consequences by simultaneously addressing these six critical dimensions of responsibility.

کلیدواژه‌ها English

Human Resource Management
Artificial Intelligence
Responsible Artificial Intelligence
[1] Conradie N, Kempt H, Königs P. Introduction to the topical collection on AI and responsibility. Philos Technol. 2022;35(4):97.
[2] Helo P, Hao Y. Artificial intelligence in operations management and supply chain management: An exploratory case study. Prod Plan Control. 2022;33(16):1573-1590.
[3] Agrawal A, McHale J, Oettl A. Finding needles in haystacks: Artificial intelligence and recombinant growth. In: Agrawal A, Gans J, Goldfarb A, editors. The economics of artificial intelligence: An agenda. Chicago: University of Chicago Press; 2018. p. 149-174.
[4] Tong S, Jia N, Luo X, Fang Z. The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strateg Manag J. 2021;42(9):1600-1631.
[5] Minbaeva D. Disrupted HR? Hum Resour Manag Rev. 2021;31(4):100820.
[6] Vrontis D, Christofi M, Pereira V, Tarba S, Makrides A, Trichina E. Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. Artificial intelligence and international HRM. 2023;172-201.
[7] Beane M. Shadow learning: Building robotic surgical skill when approved means fail. Adm Sci Q. 2019;64(1):87-123.
[8] Sergeeva AV, Faraj S, Huysman M. Losing touch: An embodiment perspective on coordination in robotic surgery. Organ Sci. 2020;31(5):1248–1271.
[9] Xie X, Liu Y, Liu J, Zhang X, Zou J, Fontes-Garfias CR, et al. Neutralization of SARS-CoV-2 spike 69/70 deletion, E484K and N501Y variants by BNT162b2 vaccine-elicited sera. Nat Med. 2021;27(4):620-621.
[10] Tambe P, Cappelli P, Yakubovich V. Artificial intelligence in human resources management: Challenges and a path forward. Calif Manag Rev. 2019;61(4):15–42.
[11] Baum K, Mantel S, Schmidt, Speith T. From responsibility to reason-giving explainable artificial intelligence. Philos Technol. 2022;35(1):12.
[12] Fuchs DJ. The dangers of human-like bias in machine-learning algorithms. Missouri S&T’s Peer to Peer. 2018;2(1). Available from: https://scholarsmine.mst
[13] Constantinescu M, Voinea C, Uszkai R, Vică C. Understanding responsibility in Responsible AI. Dianoetic virtues and the hard problem of context. Ethics Inf Technol. 2021;23:803-814.
[14] Cheng MM, Hackett RD. A critical review of algorithms in HRM: Definition, theory, and practice. Hum Resour Manag Rev. 2021;31(1).
[15] Köchling A, Wehner M. Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Bus Res. 2020;13(3):795-848.
[16] Kim PT, Bodie MT. Artificial intelligence and the challenges of workplace discrimination and privacy. ABAJ Lab & Emp L. 2020;35:289.
[17] Weston M, Sun H, Herman GL, Benotman H, Alawini A. Echelon: An AI tool for clustering student-written SQL queries. In: 2021 IEEE Frontiers in Education Conference (FIE); 2021 Oct; IEEE. p. 1-8.
[18] Jeffrey D. Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. 2018. Available from: https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G
[19] Charlwood A, Guenole N. Can HR adapt to the paradoxes of artificial intelligence? Hum Resour Manag J. 2022;32(4):729-742.
[20] Hedlund M, Persson E. Expert responsibility in AI development. AI & Soc. 2022;1-12.
[21] Wang H, Fu T, Du Y, Gao W, Huang K, Liu Z, et al. Scientific discovery in the age of artificial intelligence. Nature. 2023;620(7972):47-60.
[22] Mikalef P, Conboy K, Lundström JE, Popovič A. Thinking responsibly about responsible AI and ‘the dark side’ of AI. Eur J Inf Syst. 2022;31(3):257-268.
[23] Davenport A, Gefflot C, Beck C. Slack-based techniques for robust schedules. In: Sixth European conference on planning; 2014 May.
[24] Hakli R, Mäkelä P. Moral responsibility of robots and hybrid agents. The Monist. 2019;102(2):259-275.
[25] Loh F, Loh J. Autonomy and Responsibility in Hybrid Systems. In: Lin P, Abney K, Jenkins R, editors. Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence. Oxford: Oxford University Press; 2017. p. 35–50.
[26] Goyal A, Aneja R. Artificial intelligence and income inequality: Do technological changes and worker's position matter? Public Affairs. 2020;20(4):e2326.
[27] Servoz M. AI, the future of work? Work of the future! On how artificial intelligence, robotics and automation are transforming jobs and the economy in Europe. Publications Office; 2019.
[28] Barredo-Arrieta A, Del Ser J. Plausible counterfactuals: Auditing deep learning classifiers with realistic adversarial examples. In: 2020 International joint conference on neural networks (IJCNN); 2020 Jul. p. 1-7. IEEE.
[29] Fjeld J, Achten N, Hilligoss H, Nagy A, Srikumar M. Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. Berkman Klein Center Research Publication. 2020;(2020-1).
[30] Wiggers G, Verberne S, van Loon WS, Zwenne G. Bibliometric-enhanced legal information retrieval: combining usage and citations as flavors of impact relevance. [Journal name missing]. 2023;(8):1010-1025.
[31] Jeffrey T. Understanding college student perceptions of artificial intelligence. Systemics, Cybernetics and Informatics. 2020;18(2):8-13.
[32] Osoba O, Welser IV W. An intelligence in our image. Santa Mônica: RAND Corporation; 2017.
[33] Rana NP, Chatterjee S, Dwivedi YK, Akter S. Understanding dark side of artificial intelligence (AI) integrated business analytics: assessing firm’s operational inefficiency and competitiveness. Eur J Inf Syst. 2022;31(3):364-387.
[34] Desouza KC, Dawson GS, Chenok D. Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector. Bus Horiz. 2020;63(2):205–213.
[35] Agustono DO, Nugroho R, Fianto AYA. Artificial Intelligence in Human Resource Management Practices. KnE Soc Sci. 2023;958-970.
[36] Bankins S. The ethical use of artificial intelligence in human resource management: a decision-making framework. Ethics Inf Technol. 2021;23(4):841-854.
[37] Delecraz S, Eltarr L, Becuwe M, Bouxin H, Boutin N, Oullier O. Responsible Artificial Intelligence in Human Resources Technology: An innovative inclusive and fair by design matching algorithm for job recruitment purposes. J Responsible Technol. 2022;11:100041.
[38] Bankins S, Formosa P, Griep Y, Richards D. AI decision making with dignity? Contrasting workers’ justice perceptions of human and AI decision making in a human resource management context. Inf Syst Front. 2022;24(3):857-875.
[39] Sargiotis D. Harnessing Digital Twins in Construction: A Comprehensive Review of Current Practices, Benefits, and Future Prospects. 2024.
[40] Chang YL, Ke J. Socially responsible artificial intelligence empowered people analytics: a novel framework towards sustainability. Hum Resour Dev Rev. 2024;23(1):88-120.
[41] Chen Z. Responsible AI in Organizational Training: Applications, Implications, and Recommendations for Future Development. Hum Resour Dev Rev. 2024;23(4):498-521.
[42] Matthias A. The Responsibility Gap: Ascribing Responsibility for the Actions of Learning Automata. Ethics Inf Technol. 2004;6(3):175–183.
[43] Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277-1288.
[44] Vaismoradi M, Jones J, Turunen H, Snelgrove S. Theme development in qualitative content analysis and thematic analysis. 2016.
[45] Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107-115.
[46] Graneheim UH, Lundman B. Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Educ Today. 2004;24(2):105-112.
[47] Patton MQ. Qualitative research and evaluation methods. 3rd ed. Sage; 2002.
[48] Guest G, Bunce A, Johnson L. How many interviews are enough? An experiment with data saturation and variability. Field Methods. 2006;18(1):59-82.
[49] Patton C, Sawicki D, Clark J. Basic methods of policy analysis and planning—Pearson eText. Routledge; 2015.
[50] Norris LS, White DE, Moules NJ. Thematic analysis: Striving to meet the trustworthiness criteria. Int J Qual Methods. 2017;16(1):160940691773384.
[51] Maxwell JA. Qualitative research design: An interactive approach. 3rd ed. Sage; 2013.
[52] Creswell JW, Poth CN. Qualitative inquiry and research design: Choosing among five approaches. 4th ed. Sage; 2016.
[53] Duke T. Building responsible AI algorithms. Springer; 2023. Available from: https://link.springer.com/book/10.1007/978-1-4842-9306-5
[54] Dignum V. Responsible artificial intelligence: how to develop and use AI in a responsible way. Vol. 1. Cham: Springer; 2019.
[55] Martin K. Ethical implications and accountability of algorithms. J Bus Ethics. 2019;160(4):835-850.
[56] Yam J, Skorburg JA. From human resources to human rights: Impact assessments for hiring algorithms. Ethics Inf Technol. 2021;23(4):611-623.
[57] Bujold A, Roberge-Maltais I, Parent-Rocheleau X, Boasen J, Sénécal S, Léger PM. Responsible artificial intelligence in human resources management: a review of the empirical literature. AI Ethics. 2023;1-16.
[58] Rodgers W, Murray JM, Stefanidis A, Degbey WY, Tarba SY. An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Hum Resour Manag Rev. 2023;33(1):100925.
[59] Koenig N, Tonidandel S, Thompson I, Albritton B, Koohifar F, Yankov G, et al. Improving measurement and prediction in personnel selection through the application of machine learning. Pers Psychol. 2023;76(4):1061-1123.
[60] Tippins NT, Oswald FL, McPhail SM. Scientific, legal, and ethical concerns about AI-based personnel selection tools: a call to action. Pers Assess Decis. 2021;7(2):1.
[61] Masood A. Responsible AI in the Enterprise: Practical AI Risk Management for Explainable, Auditable, and Safe Models with Hyperscalers and Azure OpenAI. 2023.
[62] Varma A, Dawkins C, Chaudhuri K. Artificial intelligence and people management: A critical assessment through the ethical lens. Hum Resour Manag Rev. 2023;33(1):100923.
[63] Manoharan P. A Review on Cybersecurity in HR Systems: Protecting Employee Data in the Age of AI. Regul. GDPR. 2024; 4:605-612.
[64] Rocha JF. Ethical implementation of AI in job candidate recruitment: insights on data protection, Artificial Intelligence and legal perspectives. Braz J Law Technol Innov. 2024;2(1):120-139.
[65] Bryson JJ. Human Experience and AI Regulation: What European Union Law Brings to Digital Technology Ethics. Weizenbaum J Digit Soc. 2023;3(3).
[66] Raghavan M, Barocas S, Kleinberg J, Levy K. Mitigating bias in algorithmic hiring: Evaluating claims and practices. In: Proceedings of the 2020 conference on fairness, accountability, and transparency; 2020. p. 469-481.
[67] Rakova B, Yang J, Cramer H, Chowdhury R. Where responsible AI meets reality: Practitioner perspectives on enablers for shifting organizational practices. Proc ACM Hum-Comput Interact. 2021; 5:1-23.
[68] Kim PT, Bodie MT. Artificial intelligence and the challenges of workplace discrimination and privacy. ABAJ Lab & Emp L. 2020; 35:289.
[69] Lacroux A, Martin-Lacroux C. Should I trust the artificial intelligence to recruit? Recruiters’ perceptions and behavior when faced with algorithm-based recommendation systems during resume screening. Front Psychol. 2022; 13:895997.
[70] Islam M, Mamun AA, Afrin S, Ali Quaosar GA, Uddin MA. Technology adoption and human resource management practices: the use of artificial intelligence for recruitment in Bangladesh. South Asian J Hum Resour Manag. 2022;9(2):324-349.
[71] London AJ. Artificial intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent Rep. 2019;49(1):15-21.
[72] Golbin I, Rao AS, Hadjarian A, Krittman D. Responsible AI: a primer for the legal community. In: 2020 IEEE international conference on big data (Big Data); 2020. p. 2121-2126. IEEE.
[73] Sharkey A. Can robots be responsible moral agents? And why should we care? Connection Sci. 2017;29(3):210–216.
[74] Coeckelbergh M. Democracy, epistemic agency, and AI: political epistemology in times of artificial intelligence. AI Ethics. 2023;3(4):1341-1350.
[75] Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. 2017.
[76] Theodorou A, Dignum V. Towards ethical and socio-legal governance in AI. Nat Mach Intell. 2020;2(1):10–12.
[77] European Commission. General Data Protection Regulation (GDPR). Official Journal of the European Union. 2016;L119:1-88.
[78] Giermindl LM, Strich F, Christ O, Leicht-Deobald U, Redzepi A. The dark sides of people analytics: reviewing the perils for organisations and employees. Eur J Inf Syst. 2022;31(3):410-435.
[79] Selbst AD, Barocas S. The intuitive appeal of explainable machines. Fordham L Rev. 2018;87:1085.
[80] Wieringa M. What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. In: Proceedings of the 2020 conference on fairness, accountability, and transparency; 2020. p. 1-18.
[81] Simmons & Simmons and Jacob Turner. HireVue AI Explainability Statement. HireVue; 2022. Available from: https://www.hirevue.com/ai-in-hiring
[82] Application of AI in Recruitment and Selection. Artificial Intelligence in Industry 4.0: The Future that Comes True. 2024;234.
[83] Du J. Ethical and Legal Challenges of AI in Human Resource Management. J Comput Electron Inf Manag. 2024;13(2):71-77.
[84] Konidena BK, Malaiyappan JNA, Tadimarri A. Ethical Considerations in the Development and Deployment of AI Systems. Eur J Technol. 2024;8(2):41–53.
[85] Gunning D, Aha D. DARPA’s explainable artificial intelligence (XAI) program. AI Mag. 2019;40(2):44-58.
[86] Chiwara JR, Mjoli TQ, Chinyamurindi WT. Factors that influence the use of the Internet for job-seeking purposes amongst a sample of final-year students in the Eastern Cape province of South Africa. SA J Hum Resour Manag. 2017;15(1):1-9.
[87] Dastin J. Amazon scraps secret AI recruiting tool that showed bias against women. In: Ethics of data and analytics; 2022. p. 296-299.
[88] Fernández-Martínez C, Fernández A. AI and recruiting software: Ethical and legal implications. Paladyn J Behav Robot. 2020;11(1):199-216.
[89] Chen Z. Collaboration among recruiters and artificial intelligence: removing human prejudices in employment. Cogn Technol Work. 2023;25(1):135-149.
[90] Yadav S, Kapoor S. RETRACTED ARTICLE: Adopting artificial intelligence (AI) for employee recruitment: the influence of contextual factors. Int J Syst Assur Eng Manag. 2024;15(5):1828-1840.
[91] Yarger L, Cobb Payton F, Neupane B. Algorithmic equity in the hiring of underrepresented IT job candidates. Online Inf Rev. 2020;44(2):383-395.
[92] Binns R. Fairness in machine learning: Lessons from political philosophy. In: Conference on fairness, accountability and transparency; 2018 Jan. p. 149-159. PMLR.
[93] Solove DJ. Artificial intelligence and privacy. Fla L Rev. 2025;77:1.
[94] Brundage M, Avin S, Wang J, Belfield H, Krueger G, Hadfield G, et al. Toward trustworthy AI development: mechanisms for supporting verifiable claims. arXiv preprint arXiv:2004.07213. 2020.
[95] Khoa BQ. Influential factors of Artificial Intelligence (AI) in the digital transformation of the human resources recruitment process sector in Vietnam. Int J Multidiscip Res Growth Eval. 2024;5(6):1118-1193.