"Hi I'm Molly, Your Virtual Interviewer!" -- Exploring the Impact of Race and Gender in AI-powered Virtual Interview Experiences
<|reference_start|>"Hi I'm Molly, Your Virtual Interviewer!" -- Exploring the Impact of Race and Gender in AI-powered Virtual Interview Experiences: The persistent issue of human bias in recruitment processes poses a formidable challenge to achieving equitable hiring practices, particularly when influenced by demographic characteristics such as gender and race of both interviewers and candidates. Asynchronous Video Interviews (AVIs), powered by Artificial Intelligence (AI), have emerged as innovative tools aimed at streamlining the application screening process while potentially mitigating the impact of such biases. These AI-driven platforms present an opportunity to customize the demographic features of virtual interviewers to align with diverse applicant preferences, promising a more objective and fair evaluation. Despite their growing adoption, the implications of virtual interviewer identities on candidate experiences within AVIs remain underexplored. We aim to address this research and empirical gap in this paper. To this end, we carried out a comprehensive between-subjects study involving 218 participants across six distinct experimental conditions, manipulating the gender and skin color of an AI virtual interviewer agent. Our empirical analysis revealed that while the demographic attributes of the agents did not significantly influence the overall experience of interviewees, variations in the interviewees' demographics significantly altered their perception of the AVI process. Further, we uncovered that the mediating roles of Social Presence and Perception of the virtual interviewer critically affect interviewees' perceptions of fairness (+), privacy (-), and impression management (+).<|reference_end|>
@article{biswas2024"hi.,
    title={"Hi. I'm Molly, Your Virtual Interviewer!" -- Exploring the Impact of
  Race and Gender in AI-powered Virtual Interview Experiences},
    author={Shreyan Biswas, Ji-Youn Jung, Abhishek Unnam, Kuldeep Yadav, Shreyansh
  Gupta, Ujwal Gadiraju},
    journal={arXiv preprint arXiv:2408.14159},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14159},
    primaryClass={cs.HC}
}