A thematic analysis of biases in AI-enabled hiring, Manvi Kalra, Swati Tripathi

Manvi Kalra and Swati Tripathi
International Management Institute New Delhi
India

Purpose: Integration of artificial intelligence into human resource management has essentially reconfigured the organizational decisions. The purpose of this paper is to interrogate how AI-enabled hiring systems perpetuate gender biases under the cloak of neutrality.
Design/methodology/Approach: This paper deploys to two major theories Technofeminism (2004) and Data Feminism(2020). Using a systematic literature review, it explores the gendered biases in AI-enabled hiring systems by drawing from peer-reviewed articles from Scopus and Web of Science databases. Finally, thematic analysis was conducted to extract key themes of biases that emerged from the selected articles.
Findings: The findings from the review show that there is enough evidence that suggests AI reinforces gendered biases and stereotypes. Five interconnected themes emerged: gendered design; historical bias; accountability failures; perception of AI; and systemic bias. Collectively, these themes provide an understanding of the reasons that the gendered biases are embedded in AI-enabled hiring systems. These biases are not just a glitch in the system but an outcome of technical, epistemic, and organizational structures together.
Implications: This paper builds and adds to the feminist frameworks of technofeminism and data feminism by questioning how these AI-enabled hiring systems are neutral and how they reproduce gendered biases and inequality. It also offers actionable strategies for organizations and AI-developers, including participatory design, inclusion of diverse datasets, and to have gender neutral evaluation metrics. It also aligns with Sustainability Development Goal 5 on Gender Equality.
Originality: Unlike previous studies that broadly talk about gendered biases, this paper gives a feminist perspective to AI-enabled hiring systems, questioning if these biases are just a technical issue or embedded in the organizational structures. It repositions algorithmic fairness as a structural and epistemological concern in organizational management.
Keywords: Artificial Intelligence; Bias; Hiring; Recruitment; Organizations.