Most job applicants are not in favour of the use of artificial intelligence (AI) in the selection and recruitment process.
They perceive the algorithmic decision-making in the recruitment process as less fair compared to human-assisted methods, according to a survey jointly conducted by the National University of Singapore (NUS) Business School, International Institute for Management Development (IIMD) and The Hong Kong Polytechnic University.
The disparity in perceived fairness is largely attributed to AI’s inability in identifying the candidates’ unique characteristics, as compared to human recruiters who are better equipped to evaluate qualitative information that makes each candidate distinctive.
AI-enabled processes can overlook important qualities and potentially screen out good candidates. This challenges the popular notion that algorithms can provide fairer evaluations of candidates and eliminate human biases.
Additionally, there are potential legal and ethical risks involved in the use of algorithms to optimise recruitment. These may include privacy loss, lack of transparency, obfuscation of accountability and potential loss of human oversight.
“There are many benefits that AI can bring to organisations. While AI can automate any tasks traditionally done by HR, it cannot replace the human touch and interactions that are core to the HR function including recruitment exercises. The distrust of AI in providing a fair hiring assessment is prevalent. Hence, we hope our study can guide organisations to exercise caution when adopting AI in their HR recruitment processes as it may potentially lead to brand and reputational risks,” says Professor Jayanth Narayanan from the Department of Management Organisation at NUS Business School.
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The study surveyed over 1,000 participants of different nationalities comprising candidates who had experienced both successful and unsuccessful outcomes in an AI-enabled hiring process. They were involved in four scenario-based experiments, where the first two experiments studied how the use of an algorithm affects the perception of fairness amongst job applicants in the hiring process, while the remaining two sought to understand the reasons behind the lower fairness score.
