Exploring the Implementation of AI in Early Onset Interviews to Help Mitigate Bias
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916569425117184 |
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| author | Lal, Nishka Benkraouda, Omar |
| author_facet | Lal, Nishka Benkraouda, Omar |
| contents | This paper investigates the application of artificial intelligence (AI) in early-stage recruitment interviews in order to reduce inherent bias, specifically sentiment bias. Traditional interviewers are often subject to several biases, including interviewer bias, social desirability effects, and even confirmation bias. In turn, this leads to non-inclusive hiring practices, and a less diverse workforce. This study further analyzes various AI interventions that are present in the marketplace today such as multimodal platforms and interactive candidate assessment tools in order to gauge the current market usage of AI in early-stage recruitment. However, this paper aims to use a unique AI system that was developed to transcribe and analyze interview dynamics, which emphasize skill and knowledge over emotional sentiments. Results indicate that AI effectively minimizes sentiment-driven biases by 41.2%, suggesting its revolutionizing power in companies' recruitment processes for improved equity and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09890 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring the Implementation of AI in Early Onset Interviews to Help Mitigate Bias Lal, Nishka Benkraouda, Omar Artificial Intelligence This paper investigates the application of artificial intelligence (AI) in early-stage recruitment interviews in order to reduce inherent bias, specifically sentiment bias. Traditional interviewers are often subject to several biases, including interviewer bias, social desirability effects, and even confirmation bias. In turn, this leads to non-inclusive hiring practices, and a less diverse workforce. This study further analyzes various AI interventions that are present in the marketplace today such as multimodal platforms and interactive candidate assessment tools in order to gauge the current market usage of AI in early-stage recruitment. However, this paper aims to use a unique AI system that was developed to transcribe and analyze interview dynamics, which emphasize skill and knowledge over emotional sentiments. Results indicate that AI effectively minimizes sentiment-driven biases by 41.2%, suggesting its revolutionizing power in companies' recruitment processes for improved equity and efficiency. |
| title | Exploring the Implementation of AI in Early Onset Interviews to Help Mitigate Bias |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2501.09890 |