The New Architects of Hiring: AI Platforms and the Shifting Stakeholder Balance
Hiring decisions are no longer made between two humans alone โ they're shaped by a three-way ecosystem powered by algorithms.
Farah Mitchell
The hiring world, with the integration of artificial intelligence, is transcending its traditional boundaries and evolving into a far more complex ecosystem. This transformation doesn't just affect the relationship between job seekers and employers; it also introduces a highly powerful and decisive third party into the process: "recruitment platforms." Hiring decisions are no longer shaped solely by the interaction between two humans, but by the sophisticated algorithms that these platforms develop and market. These new stakeholders are taking the stage as the hidden architects who determine the outcomes of the labor market.
The triple-stakeholder structure in the hiring ecosystem
With the rise of artificial intelligence, a new stakeholder group called "recruitment platforms" is joining the hiring process alongside employers and candidates. These technology providers, with the solutions they develop, are becoming the gold standard of the global market and beginning to set the direction for the entire industry.
The study by Marchetti and Scardovi (2024) emphasizes that AI solutions' ability to analyze thousands of resumes in seconds and predict a candidate's likelihood of success is transforming hiring into an entirely data-driven endeavor. Analyzing the interview processes of major corporations like Sace and Vodafone, Marchetti et al. (2024) note that these platforms don't just schedule interviews -- they also generate competency data claimed to be "objective," derived from candidates' vocal tone and facial expressions.
As highlighted in the University of Sussex (2025) toolkit, it is vital for brand credibility that employers deeply understand how the platforms they use encode their hiring criteria into algorithms. The research by Diyin, Bhaumik, and Wang (2024) states that while these platforms' promise of significantly reducing operational costs attracts employers, great care must be taken regarding the ethical use of such systems.
The transparency gap and the need for a "Glass Box"
University of Sussex (2025) data documents that candidates have a very weak understanding of how the technology evaluates them, leading them to perceive the process as "dehumanizing." Jaser et al. (2025) argue that the fundamental way to bridge this digital divide is to adopt a "glass box" approach that is fully transparent about how the technology is used. Thanks to the glass box approach, candidates can deliver their best performance by knowing what is being scored, while employers gain fair access to a broader and more diverse talent pool.
Savani and colleagues (2022) note that candidates hold a belief that algorithms cannot recognize their "uniqueness," and that this belief triggers resistance toward algorithmic processes. The University of Sussex (2025) toolkit study emphasizes that rather than merely touting the benefits of technology, platforms have an ethical obligation to clearly disclose to candidates what the AI measures on behalf of the employer. The GFI Group Limited (2025/26) report states that platforms and employers that pursue a transparent communication strategy gain a significantly stronger competitive position in the war for talent.
Conclusion: Combining human touch with technological power
Research shows that even when candidates see AI speed at the beginning of an interview, seeing a human face at the end is accepted as "proof that they are valued." Jaser (2025) notes that for platforms to develop ethical algorithms that also consider candidate interests is no longer merely a choice, but a necessity for a sustainable business model.
In this new multi-stakeholder order of hiring, success belongs to those who can blend the speed of algorithms with human empathy and ethical oversight.
References
- Diyin, Z., Bhaumik, A., & Wang, D. (2024). Artificial Intelligence's Impact on Hr and Talent Acquisition. Journal of Electrical Systems, 20-11s, 4879-4885.
- Drela, K., Grabowska, A., & Brojak-Trzaskowska, M. (2025). The Future of AI in HRM. A Case Study of the HR Decision-Making. 28th European Conference on Artificial Intelligence (ECAI 2025).
- GFI Group Limited. The Ethical Use of AI and Automation in Recruitment (2025/26).
- Jaser, Z., Petrakaki, D., Starr, R., Oyarbide, E., Williams, J., & Newton, B. (2025). Artificial Intelligence (AI) in the job interview process: Toolkit for employers, careers advisers and hiring platforms. University of Sussex & Institute for Employment Studies.
- Jurado, N. (2025). The effects of artificial intelligence on shaping employer brand perception: insights from entry-level hiring practices. Master Thesis, Universidad Carlos III de Madrid.
- Marchetti, D., & Scardovi, R. (2024). Artificial Intelligence and Human Resources: innovative trends and main impacts. Master Thesis, Politecnico di Milano.
- Savani, K., Lavanchy, M., Reichert, P., & Narayanan, J. (2022). Applicants' Fairness Perceptions of Algorithm-driven Hiring Procedures. IMD & NUS Business School.
- Tuffaha, M. (2025). Adoption Factors of Artificial intelligence in Human Resource Management. Universitat Politecnica de Valencia.
- Venkanna, G., Yogesh, D. M., Rao, J. Y., & Preetham, K. S. (2024). Smart Applicant Tracking Systems in the Future. International Journal of Computer Science and Network Security.