Unpacking the Double-Edged Sword: How Artificial Intelligence Shapes Hiring Process Through Biased HR Data

Abstract

Artificial intelligence (AI) has transformed the field of hiring, enabling employers to collect and analyse massive amounts of data to understand and predict the suitability of candidates. However, AI can also have subconscious effects on candidates’ and employers needs through biased data, which can stem from human biases, algorithmic errors or external factors. For example, Amazon scrapped an AI-based recruitment programme that favoured male candidates over female candidates due to the historical patterns in the resumes it analysed. This paper examines how AI can shape candidate's needs through biased data from various sources and types, and what are the consequences for candidate's welfare and rights. We review the literature on AI applications in hiring, the origins and kinds of bias in AI systems, and the potential risks and benefits for candidates. We also suggest some guidelines for reducing bias in AI and enabling candidates to make informed and ethical choices online. We argue that AI can be a double-edged sword for candidate's needs and that more research and regulation are required to ensure its fair and accountable use.

Citation

Amin Metwally Hussien, O.A., Hasanaj, K., Kaya, A., Jahankhani, H. and El-Deeb, S. (2024), "Unpacking the Double-Edged Sword: How Artificial Intelligence Shapes Hiring Process Through Biased HR Data", Dadwal, S.S., Jahankhani, H. and Revett, K. (Ed.) Market Grooming, Emerald Publishing Limited, Leeds, pp. 97-119. https://doi.org/10.1108/978-1-83549-001-320241005