Algorithmic hiring is commonly used to supplement the hiring processes across the tech industry. In this study, we implement a mixed-methods design to develop a detailed understanding of the algorithms powering candidate search tools. Our study consists of two parts: interviews with industry professionals and an algorithmic audit. Throughout Fall 2022, our team conducted semi-structured interviews with current technical recruiters and recruiting managers. These interviews provided a more holistic understanding of the recruitment process and underscored the importance of candidate search tools in determining candidate hiring outcomes. This spring, we will further investigate one such tool, HireEZ, through an algorithmic audit. Through this audit, we aim to assess the key markers used by the company’s algorithms to identify and rank suitable candidates. Ultimately, this research provides insight into how companies construct and operationalize concepts such as talent and role suitability within the tech industry, and how these constructions may privilege or oppress certain demographic groups. Additionally, this research critically assesses how search algorithms may operationalize and perpetuate these concepts. Taken together, this research contributes to an understanding of the broader implications of the tech industry’s shift towards AI-based recruiting, and informs visions for the future of the recruitment industry.
Grant / January 2023