Graduation Year

2021

Date of Submission

11-2020

Document Type

Open Access Senior Thesis

Degree Name

Bachelor of Arts

Reader 1

Professor Bowman

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Rights Information

© 2020 Emma David

OCLC Record Number

1241987731

Abstract

This paper serves as a literature review of artificial intelligence (AI) in the hiring process to examine the extent to which AI tools reliably, accurately, and fairly select talent. Today’s organizations are under immense pressure to develop innovative hiring strategies to identify and secure talent for their workforce. As technological advances arise, companies are increasingly turning to AI to aid in their hiring efforts. Hirers are enticed by AI because there is increased systematicity in determining the most qualified workers with AI as opposed to current selection methods that rely on intuitively combining and weighting applicant information. In an experimental study included in this paper, the researchers showed that increasing systematicity in decision-making processes leads to higher quality decisions. I then delve into specific use cases of AI such as processing applicants’ narrative responses and scraping applicants’ social media accounts. While it is possible for AI to execute these endeavors, researchers investigated whether AI could do so consistently and accurately. In one study, researchers demonstrated that the computer program they had trained exhibited a level of reliability comparable to that of a human rater and that the computer scores also exhibited construct validity. While establishing reliability and validity is crucial, it is also important to consider applicants’ perceptions towards AI. In one study, applicants generally held negative perceptions toward AI in the hiring process, but interestingly, their distrust was reduced when they possessed more knowledge and experience with AI. Lastly, this paper assesses whether AI is indeed as objective as it seems. Due to the nature of how AI programs are trained, it is likely that human biases will emerge in the seemingly objective process. Multiple real-world examples revealed that AI has led to biased and discriminatory outputs, which is a major concern since the implementation of AI in the hiring process would directly impact workforce diversity and individuals’ well-being at a greater scale than its human counterparts. It is possible that if the implementation of AI in the hiring process goes unchecked, socio-economic divides will be exacerbated. I therefore suggest that Industrial/Organizational psychologists be included in the development of AI hiring tools and that the companies developing and using AI tools for hiring purposes should be held accountable, specifically through publicly accessible algorithmic reviews.

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