Research from King's Business School, published in the journal Information Technology & People, examined interviews with 41 participants including technology experts, HR professionals, AI companies, and advisers involved in AI-enabled hiring. The study set out not to test the accuracy of individual AI tools, but to understand how the people deploying them think and talk about bias, risk, and responsibility.
Professor Elisabeth Kelan identified two positions that participants moved between, often within the same interview. The first, which she terms techno-optimism, presents AI as a mechanism for reducing the biases already embedded in human hiring: more representative training data, anonymised application processing, algorithmic audits. The second, techno-hesitation, is more nuanced. When participants acknowledged the limits of AI or the risk of discriminatory outcomes, that acknowledgement rarely led to opposing adoption. Instead, ethical concerns were reframed as legal exposure, reputational risk, or managerial problems that could be managed with the right safeguards.
The practical effect is that caution and concern end up supporting continued AI use: by demonstrating awareness of the risks and putting control mechanisms in place, organisations present themselves as responsible adopters. Questions about whether AI hiring is actually fairer than human hiring remain, in the study's framing, largely unanswered — or at least unasked.
"The research suggests that debates about AI hiring are not simply divided between techno-optimism and techno-pessimism. Many participants acknowledged concerns about bias while simultaneously supporting the use of these technologies. What emerged was a form of techno-hesitation, where ethical concerns were reframed as legal, reputational or managerial risks that organisations could manage," said Elisabeth Kelan, Professor of Leadership and Organisation at King's Business School.
For employers, the study's practical recommendation is to start with the hiring problem and the fairness test before selecting the technology: what data will the AI use, how will outcomes be tested across different demographic groups, who is accountable, who can override it, and how can applicants challenge a decision. For jobseekers, Professor Kelan suggests asking whether AI will be used to screen or score their application, what information will be assessed, and whether humans remain involved in the final decision.
The paper draws on interviews with participants in the UK, Europe, the US, Australia, and the UAE.
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