The talent acquisition landscape stands on the cusp of radical transformation as artificial intelligence (“AI”) rapidly evolves. Advances in generative AI models capable of producing original text, images, audio and more have generated tremendous excitement about their potential to reshape recruiting in inclusive and ethical ways. This article will analyse how generative AI is already affecting applicants, recruiters and hiring managers. It will explore the steps organisations must take to rigorously audit these systems. This oversight is crucial to ensure generative models deliver on their promises to improve diversity, equity, and inclusion rather than inadvertently compromise them.

Overview

While still in its early stages, generative AI has already demonstrated promising capabilities that have been on recruiters’ radars for over a year now. However, the full impacts of this developing technology remain uncertain. As generative AI continues its meteoric pace of advancement, we find ourselves at a critical juncture.

Now is the time to thoughtfully evaluate these emerging models, build understanding of their current abilities and limitations, and prepare to embrace the changes generative AI may bring – both positive and negative.

Even with the uncertainties, one thing is clear: generative AI has the potential to drastically change the recruiting landscape in the future. Organisations may use it to improve talent acquisition and other talent management processes now if they are diligent and proactive.

Advantages of using AI in recruitment:

Generative AI is already demonstrating ability to rewrite job postings free of biased language, screen candidates in unbiased ways focusing on skills over , match internal employees to new roles, and provide personalised support through recruiting chatbots. These applications open opportunities for underrepresented groups, challenging traditional barriers in hiring.

  • For applicants: generative AI reaches talent pools more broadly by targeting capabilities rather than academic pedigree or employer brand. Masking demographic data during resume screening counters unconscious bias. AI interviewers allow applicants to highlight qualifications on an equal footing. Chatbots provide guidance throughout the process improving candidate experience.
  • For talent acquisition teams: AI automates high-volume administrative tasks, allowing recruiters to focus on relationship-building and evaluation. Generative AI also surfaces non-traditional candidates from wider networks. This expands and diversifies the pipeline.
  • For hiring managers: AI reduces dependence on schools, past employers and other proxies that narrowly limit applicant pools. Generative screening qualifies candidates based on skills assessments, not just credentials. This focus on ability over pedigree counters historical biases. AI analyses vast amounts of data to identify patterns and trends, assisting to make more informed decisions and improve overall workforce performance. Hiring managers are likely to have access to a broader and more diverse talent pool tool.
  • For internal mobility: Internally, AI helps match employees to new roles by evaluating transferable skills and interests. This expands advancement opportunities beyond traditional paths (Benbya et al., 2020).

 

Mitigating risks of using AI in recruitment:

But without diligence, AI risks replicating human biases. Discriminatory data, algorithms, lack of human touch, ethical considerations, overreliance on technology and implementation practices compromise promised benefits. Organisations must thoroughly vet AI systems, including asking vendors the questions below:

  • How can solutions be customised to different organisations?
  • What are the data privacy and security measures in place to protect candidates’ data?
  • How do you ensure AI reduces bias in screening and selecting candidates?
  • What ethical checks are in place to maintain fairness and transparency?
  • How will our specific diversity, equity and inclusion needs be addressed?
  • How can we audit AI performance over time to prevent “decay” of models?
  • What training and documentation supports proper and responsible use of your AI tools?

By asking these questions, organisations can gain a better understanding of the capabilities, limitations, and ethical considerations associated with AI vendors providing talent management solutions. It is essential to thoroughly evaluate potential vendors to ensure a successful and responsible integration of AI in the recruitment process.

Conclusion

Generative AI may help organisations attract, retain, and develop more diverse, inclusive, and equitable talent pipelines with stringent auditing and human oversight. However, AI is not a silver bullet. Recruitment teams are ultimately in charge of maintaining inclusive, ethical, and comprehensive procedures. As with any revolutionary technology, achieving AI’s full potential demands careful application. When applied with a spirit of opportunity and caution, generative models can assist organisations in removing long-standing obstacles to hire a wide pool of talent more effectively.

Need help?

With extensive expertise in workforce planning and talent acquisition, C&G consultants are well-equipped to help firms navigate evolving regulatory complexity. Our team has held senior roles at top UK brokerages, giving firsthand insight into industry talent needs. We leverage this experience to assist companies in strengthening internal oversight capabilities amid new regulations and reforms. Our services span workforce planning, talent acquisition and in-house training. For additional details on collaborating with us as your workforce planning and talent partner, please contact us.

References:

www.bbc.com. (n.d.). AI in 2024: Five trends workers need to know. [online] Available at: https://www.bbc.com/worklife/article/20240104-ai-in-2024-five-trends-workers-need-to-know (last accessed 8th January 2024).

Benbya, H., Pachidi, S., & Jarvenpaa, S. L. (2020). Artificial Intelligence in Organizations: Implications for Information Systems Research. Journal of the Association for Information Systems, 22(2), pp. 518-555.

Dattner, B., Chamorro-Premuzic, T., Buchband, R. and Schettler, L. (2019). The Legal and Ethical Implications of Using AI in Hiring. [online] Harvard Business Review. Available at: https://hbr.org/2019/04/the-legal-and-ethical-implications-of-using-ai-in-hiring (last accessed 8th January 2024).

Leutner, F., Akhtar, R. and Chamorro-Premuzic, T. (2022), “The Far Future (Possibilities beyond Tomorrow)”, The Future of Recruitment (The Future of Work), Emerald Publishing Limited, Leeds, pp. 171-196.

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