Hiring & Discrimination in the times of AI

Algorithmic screening is increasingly used in talent acquisition across large and small corporations worldwide, with an ambitious promise of eliminating idiosyncratic human biases by using identical and consistent evaluation criteria. Nonetheless, very little is known about how these algorithms are trained across a myriad of behavioral dimensions, from texts in CVs and interviews to non-verbal features in interviews. This lack of evidence and transparency raises serious concerns among job seekers and policymakers, particularly when it comes to leveling the playing field for ethnic minorities and female job applicants. Importantly, there is virtually no evidence of how job seekers perceive and respond to these automated procedures, as well as how professional recruiters assess persuasion strategies from candidates of various backgrounds, in combination with algorithmic tools. This project seeks to address these pressing issues by conducting a series of field and online survey experiments using a combination of causal inference and NLP methods. Answering these questions would enable us to develop effective policies around AI in the recruitment processes, by uncovering the black boxes of application & recruitment strategies of job seekers and HR professionals in the face of AI.