Negotiating algorithmic bias: discourses of techno-optimism and techno-hesitation in AI-mediated hiring

Purpose Although artificial intelligence (AI)-mediated hiring is often viewed as a way to reduce human bias, it is commonly singled out as an example of algorithmic bias, which creates and amplifies inequalities. This article explores how the use of AI in hiring can be justified despite the existence of algorithmic bias. Design/methodology/approach Interviews and discourse analysis were conducted to examine how the use of AI in hiring can be justified despite algorithmic bias. Findings The interviewees expressed both techno-optimism and techno-hesitation when talking about AI in hiring. Techno-optimism emphasises that people make hiring processes biased and suggests that algorithmic bias can be addressed through a range of techniques such as fixing the data, fixing the human, “blinding” the algorithm and various processes of quality control and auditing algorithms. Rather than adopting techno-pessimism, the interviewees expressed techno-hesitation. Techno-hesitation refers to the acknowledgement of ethical concerns, followed by their reframing as managerial risks. Techno-optimism and techno-hesitation work together to justify the use of AI in hiring despite the risk of algorithmic bias. Originality/value Through theorising techno-hesitation, this article advances the literature by showing how ethical concerns can function as a resource for sustaining rather than disrupting AI adoption. The study contributes a novel analytical lens for understanding how legitimacy is discursively produced through the management, rather than the resolution, of ethical tensions surrounding AI-mediated hiring.