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Paper Number
2583
Paper Type
Complete
Description
Asynchronous video interviews (AVIs) provide scalable, low-cost opportunities for matching interviewees and organizations. However, the implications of a shift from synchronous interviews aren’t fully understood, especially when design choices such as AI evaluations are employed. To better understand the impact of AVIs, we undertook an exploratory qualitative study in addition to an experiment. The first study involves 100 qualitative responses and exploratory quantitative tests on the relationships between coded values and demographic and trait variables of the respondents. Our second study tests the impact of AI feedback using a large online AVI service while accounting for various disadvantaged groups that could experience discrimination in their AVI interactions. We developed 5 propositions regarding the interaction of interviewee traits and AVI design. Additionally, we did not find support that AI feedback increases the performance of interviewees, though we identify several traits that lead to high AI scores and human-rater performance.
Recommended Citation
Skousen, Tanner; Steffen, Jacob; Chandler, Cherileigh L.; Rosengren, Warren; Gaskin, James; and Meservy, Tom, "Asynchronous Video Interviews and Artificial Intelligence: A Multi-Study Exploration" (2022). ICIS 2022 Proceedings. 17.
https://aisel.aisnet.org/icis2022/is_futureofwork/is_futureofwork/17
Asynchronous Video Interviews and Artificial Intelligence: A Multi-Study Exploration
Asynchronous video interviews (AVIs) provide scalable, low-cost opportunities for matching interviewees and organizations. However, the implications of a shift from synchronous interviews aren’t fully understood, especially when design choices such as AI evaluations are employed. To better understand the impact of AVIs, we undertook an exploratory qualitative study in addition to an experiment. The first study involves 100 qualitative responses and exploratory quantitative tests on the relationships between coded values and demographic and trait variables of the respondents. Our second study tests the impact of AI feedback using a large online AVI service while accounting for various disadvantaged groups that could experience discrimination in their AVI interactions. We developed 5 propositions regarding the interaction of interviewee traits and AVI design. Additionally, we did not find support that AI feedback increases the performance of interviewees, though we identify several traits that lead to high AI scores and human-rater performance.
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