Navigating HR 4.0: Harnessing AI for Ethical and Inclusive HR Transformation †
1
Department of Information Technology and Management Systems, Faculty of Business Administration, Al Maaref University, Beirut 1600, Lebanon
2
Department of Management, Faculty of Business Administration, Al Maaref University, Beirut 1600, Lebanon
3
Department of Computer Science and Engineering (CSE), Kuwait College of Science and Technology, Al Doha Area 35001, Kuwait
*
Author to whom correspondence should be addressed.
†
Presented at the International Scientific Conference on Digitalization, Innovations & Sustainable Development: Trends and Business Perspectives, West Mishref, Kuwait, 29 November & 14 December 2023.
Proceedings 2024, 101(1), 18; https://doi.org/10.3390/proceedings2024101018
Published: 23 May 2024
(This article belongs to the Proceedings of International Scientific Conference on Digitalization, Innovations & Sustainable Development: Trends and Business Perspectives 2023)
1. Objectives
In the Industry 4.0 era, AI-driven HR automation is pivotal for staying competitive. It transforms talent acquisition, work processes, and employee interactions [1,2,3,4,5]. However, it also introduces complex ethical and inclusivity considerations that require careful navigation to realize its full potential [6,7,8]. Organizations are increasingly recognizing the need to adopt AI-powered HR solutions to thrive in this rapidly evolving landscape [9,10,11,12,13,14,15].
2. Methodology
In-depth research into HR 4.0 and AI integration follows a meticulous methodology, emphasizing ethics and inclusivity. It includes a comprehensive literature review, data collection from diverse sources, analysis of ethical and inclusive challenges, the development of ethical strategies, the creation of a framework model, and validation through real-world case studies. This approach ensures responsible AI adoption in HR, fostering diversity and ethical practices.
3. Results
In HR 4.0, AI integration with ethics is vital. Using a structured framework, organizations foster responsible AI integration. This includes ethical leadership, data management, transparency, employee education, policies, collaboration, inclusivity, evaluation, external auditing, and community engagement. Upholding ethics and diversity cultivates a responsible and innovative workplace culture, enhancing competitiveness in HR 4.0.
4. Implications
This study profoundly impacts businesses integrating AI into HR 4.0. It offers practical guidance, addresses ethical considerations, and emphasizes inclusivity. By safeguarding against risks, fostering fairness, and respecting individual needs, it equips organizations to navigate AI integration while upholding core values, transparency, and inclusiveness, ensuring that AI reaches its full potential in HR 4.0.
5. Originality Value
This study’s originality resides in the comprehensive exploration of AI’s moral implications in HR 4.0. Offering a structured approach, it enhances the likelihood of successful AI-driven HR transformations grounded in ethics and inclusiveness. By tackling actual problems via AI, valuable insights into HR management are provided to industry leaders and subject matter experts.
6. Contribution
The study “Navigating HR 4.0: Harnessing AI for Ethical and Inclusive HR Transformation” offers vital insights related to the following areas:
- Bridging the AI–HR gap for ethical integration: This point clearly states the study’s role in connecting AI technologies with HR practices in a way that emphasizes ethical considerations.
- Promoting ethical AI in HR through practical tactics: This suggests that the study not only discusses ethical AI but also provides actionable tactics for its implementation in HR settings.
- Fostering inclusive workplaces via user-centric design: Indicates the study’s focus on designing AI tools that are inclusive and centered around the user’s needs, contributing to more equitable workplace environments.
- Providing practical advice for implementation: Highlights the study’s aim to offer direct, actionable guidance for integrating AI into HR operations.
- Providing empirical evidence for practical applicability: Affirms that the study includes real-world evidence supporting the practical use of ethical AI in HR.
- Advocating for cross-disciplinary collaboration: The study encourages collaboration across different fields to enhance AI integration in HR, indicating a holistic approach.
- Encouraging stakeholder engagement: Emphasizes the importance of involving various stakeholders in the AI integration process, ensuring diverse inputs and buy-in.
- Equipping HR for future tech advancements: Prepares HR professionals for future technological developments, ensuring they remain at the forefront of innovation in their field.
Author Contributions
All authors, K.D., A.E.D. and H.F.-K. equally contributed to every phase of the research, including conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, visualization, supervision, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable. The study did not involve human participants, clinical trials, or animals.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Chowdhury, S.; Dey, P.; Joel-Edgar, S.; Bhattacharya, S.; Rodriguez-Espindola, O.; Abadie, A.; Truong, L. Unlocking the value of artificial intelligence in human resource management through AI capability framework. Hum. Resour. Manag. Rev. 2023, 33, 100899. [Google Scholar] [CrossRef]
- Diakopoulos, N. Accountability in algorithmic decision making. Commun. ACM 2016, 59, 56–62. [Google Scholar] [CrossRef]
- Davenport, T.H. The Rise of AI Makes Emotional Intelligence More Important; Harvard Business Publishing: Brighton, MA, USA, 2018. [Google Scholar]
- Khang, A.; Jadhav, B.; Birajdar, S. Industry Revolution 4.0: Workforce Competency Models and Designs. In Designing Workforce Management Systems for Industry 4.0; CRC Press: Boca Raton, FL, USA, 2023; pp. 11–34. [Google Scholar]
- Kshetri, N. Data privacy in the age of digitalization. Telecommun. Policy 2019, 43, 101817. [Google Scholar]
- Mittelstadt, B.D.; Allo, P.; Taddeo, M.; Wachter, S.; Floridi, L. The ethics of algorithms: Mapping the debate. Big Data Soc. 2016, 3, 303–341. [Google Scholar] [CrossRef]
- Mithas, S.; Tafti, A.; Bardhan, I.; Goh, J.M. Information technology and firm profitability: Mechanisms and empirical evidence. Manag. Sci. 2019, 58, 641–654. [Google Scholar] [CrossRef]
- Raghavan, A.; Deterding, S. From diversity and inclusion to justice and equity. Interactions 2019, 26, 64–67. [Google Scholar]
- Barocas, S.; Hardt, M.; Narayanan, A. Fairness and Machine Learning; The MIT Press: Cambridge, MA, USA, 2023. [Google Scholar]
- Benbasat, I.; Goldstein, D.K.; Mead, M. The case research strategy in studies of information systems. MIS Q. 2018, 11, 369–386. [Google Scholar] [CrossRef]
- Bersin, J. HR Technology 2020: Disruption Ahead. Available online: https://joshbersin.com/hr-tech-disruptions-for-2019/ (accessed on 28 November 2023).
- Broekhuizen, T.; Dekker, H.; de Faria, P.; Firk, S.; Nguyen, D.K.; Sofka, W. AI for managing open innovation: Opportunities, challenges, and a research agenda. J. Bus. Res. 2023, 167, 114196. [Google Scholar] [CrossRef]
- Chen, M.; Mao, S.; Liu, Y. Big data: A survey. Mob. Netw. Appl. 2018, 19, 171–209. [Google Scholar] [CrossRef]
- Kraiger, K.; Ford, J.K.; Salas, E. Application of cognitive, skill-based, and affective theories of learning outcomes to new methods of training evaluation. J. Appl. Psychol. 2019, 88, 322–336. [Google Scholar] [CrossRef]
- Marler, J.H.; Boudreau, J.W. An evidence-based review of HR Analytics. Int. J. Hum. Resour. Manag. 2017, 28, 3–26. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Danach, K.; El Dirani, A.; Fayyad-Kazan, H. Navigating HR 4.0: Harnessing AI for Ethical and Inclusive HR Transformation. Proceedings 2024, 101, 18. https://doi.org/10.3390/proceedings2024101018
AMA Style
Danach K, El Dirani A, Fayyad-Kazan H. Navigating HR 4.0: Harnessing AI for Ethical and Inclusive HR Transformation. Proceedings. 2024; 101(1):18. https://doi.org/10.3390/proceedings2024101018
Chicago/Turabian StyleDanach, Kassem, Ali El Dirani, and Hasan Fayyad-Kazan. 2024. "Navigating HR 4.0: Harnessing AI for Ethical and Inclusive HR Transformation" Proceedings 101, no. 1: 18. https://doi.org/10.3390/proceedings2024101018
