AI in Recruitment: The Candidate Perspective

Guides & Reports Carl-Johan Holmberg

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In step with the rapid technological development, more and more organisations are implementing AI in their recruitment processes. This trend is often highlighted from the employer’s perspective, emphasising benefits such as time and cost savings, along with enhanced objectivity and quality in the hiring process.

Yet, amidst the buzz surrounding AI in recruitment, one crucial aspect remains largely overlooked: the perspective of the candidates.

Key Points:

  • When implementing AI-enabled recruitment systems, employers risk overlooking the candidate experience.

  • Providing a poor candidate experience can scare away competent job seekers.

  • In order to achieve a good candidate experience, it is important to consider how and where in the recruitment process AI is implemented.

The Rationale Behind the Implementation of AI in Recruitment

For employers, the implementation of AI in recruitment is driven by incentives such as increasing objectivity in hiring decisions, saving time and reducing costs.1 Studies have shown that information about candidates’ age, ethnicity and disabilities tends to affect how recruiters judge them.2,3,4 Here, the idea is that AI-enabled recruitment tools can mitigate such human biases and thus enhance the quality of the recruitment process.

Regarding the time-saving aspect, AI can scan and analyse candidate data in a fraction of the time it would take a human recruiter, thus reducing time-to-hire. By automating tasks that would otherwise be performed by recruiters, AI can save human capital and thus reduce costs related to recruitment.

Although the aforementioned arguments appear to be strong reasons why organisations should implement AI in their recruitment processes, it is also important to consider the perspective of the candidates.

The Importance of a Good Candidate Experience🧡 

Candidate experience refers to how a job applicant perceives an employer throughout the recruitment process. A poor candidate experience can hurt organisations in many ways. For example, studies have shown that job applicants who have had a bad candidate experience are less likely to purchase products from that company in the future, and they are also less likely to seek employment at that company again.5

Studies have also shown that candidates are inclined to share their bad candidate experience with others.6 Thus, in times of skills shortage, employers simply cannot afford to provide poor candidate experiences.

Perceived Fairness is Key🫱🏽‍🫲🏼

There is a lot of empirical evidence showing that perceived fairness in the recruitment process is a key ingredient for a good candidate experience.5,7 A recruitment process is usually perceived as fair if:

  • Every candidate is being treated equally based on their performance.
  • The candidates are provided with enough information about the recruitment process.
  • The selection methods are objective.

The AI Fairness Paradox

Although AI can reduce human biases and increase objectivity in the recruitment process, the use of AI can paradoxically reduce perceived fairness. In a large-scale survey in the European Union8, a majority of the respondents agreed with the following statement:

“Algorithms might be objective, but I feel uneasy if computers make decisions about me. I prefer humans making those decisions.”

A study on the use of AI evaluations in employment interviews found that professionals preferred human evaluators.10 A possible explanation for this is that it is easier to relate to and understand a human rater (with human biases) than an algorithm. Another study on employment interviews found that people perceived the opportunity to perform as lower when they expected that their interview responses would be evaluated automatically.11

Moreover, studies have shown that people perceive AI to lack the ability to account for each human being's unique characteristics (e.g., attitudes and potential).12,13 This suggests that perceived fairness requires more than eliminating biases—it requires a holistic consideration of human characteristics.

How to Succeed With AI in Recruitment🚀

The implementation of AI in recruitment processes is a relatively new phenomenon. Therefore, research in this area lags behind the rapidly evolving technology.14 However, based on the studies that have been conducted so far, it is possible to give some general tips to ensure a good candidate experience when using AI in recruitment processes.

Mix AI With Humans

A central aspect to consider is how and where in the recruitment process AI is implemented. Using AI to generate job descriptions or summarise the content of resumes is not the same as letting AI make hiring decisions. Allowing AI to make important decisions can arouse negative feelings in candidates.15 As mentioned earlier, although decisions made by AI may be more objective than decisions made by humans, people are sceptical about letting AI make important decisions about them.8

Scientific findings indicate that involving humans in the final hiring decision increases candidates’ perceptions of fairness.7

Describe Your Recruitment Process as Trendy

A study on candidates’ views on AI showed that the more the candidates viewed AI-enabled recruitment as trendy, the more likely they were to engage with and complete the recruitment process.16 Therefore, to reinforce that your recruitment process is trendy, and thus enhance the candidate experience, you can use lines such as “Our use of AI in recruitment is at the leading edge.”.

Hope you found the content interesting! For more Recruiting Lab Notes and other interesting guides and reports, visit our blog 👋🏼 

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3 Bjørnshagen, V., & Ugreninov, E. (2021). Disability disadvantage: experimental evidence of hiring discrimination against wheelchair users. European Sociological Review, 37(5), 818-833.

4 Carlsson, M., & Rooth, D. O. (2007). Evidence of ethnic discrimination in the Swedish labor market using experimental data. Labour Economics, 14(4), 716-729.

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8 Grzymek, V., & Puntschuh, M. (2019). What Europe knows and thinks about algorithms. Bertelsmann Stiftung.

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11 Langer, M., König, C. J., & Hemsing, V. (2020). Is anybody listening? The impact of automatically evaluated job interviews on impression management and applicant reactions. Journal of Managerial Psychology, 35(4), 271-284.

12 Newman, D. T., Fast, N. J., & Harmon, D. J. (2020). When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes, 160, 149-167.

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