A Thematic Analysis of Biases in AI-enabled Hiring, Swati Tripathi
Swati Tripathi
Assistant Professor, OB/HR Area
International Management Institute New Delhi
India
DOI: 10.47556/B.OUTLOOK2026.24.1
Abstract
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. 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. 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. 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. 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