When AI is Perceived to Be Fairer than a Human: Understanding Perceptions of Algorithmic Decisions in a Job Application Context

Journal article

Hyesun Choung, John S. Seberger, Prabu David
International journal of human computer interactions, 2023

Cite

APA Click to copy
Choung, H., Seberger, J. S., & David, P. (2023). When AI is Perceived to Be Fairer than a Human: Understanding Perceptions of Algorithmic Decisions in a Job Application Context. International Journal of Human Computer Interactions.

Chicago/Turabian Click to copy
Choung, Hyesun, John S. Seberger, and Prabu David. “When AI Is Perceived to Be Fairer than a Human: Understanding Perceptions of Algorithmic Decisions in a Job Application Context.” International journal of human computer interactions (2023).

MLA Click to copy
Choung, Hyesun, et al. “When AI Is Perceived to Be Fairer than a Human: Understanding Perceptions of Algorithmic Decisions in a Job Application Context.” International Journal of Human Computer Interactions, 2023.

BibTeX Click to copy

@article{hyesun2023a,
  title = {When AI is Perceived to Be Fairer than a Human: Understanding Perceptions of Algorithmic Decisions in a Job Application Context},
  year = {2023},
  journal = {International journal of human computer interactions},
  author = {Choung, Hyesun and Seberger, John S. and David, Prabu}
}

Abstract

Abstract This study investigates people’s perceptions of AI decision-making as compared to human decision-making within the job application context. It takes into account both favorable and unfavorable outcomes, employing a 2 × 2 experimental design (decision-making agent: AI algorithm vs. human; outcome: favorable vs. unfavorable). Upon evaluating a job seeker’s suitability for a position, participants viewed algorithmic decisions as fairer, more competent, more trustworthy, and more useful than those made by humans. Interestingly, when a candidate was deemed unsuitable for hiring, people reacted more negatively to the verdict given by a human than to the same judgment offered by AI. Moreover, participants credited algorithmic decisions with greater sensitivity to both quantitative and qualitative qualifications, thus indicating algorithmic appreciation. Our findings shed light on the psychological basis of perceptions surrounding Algorithmic Decision-Making (ADM) and the responses to the decisions rendered by ADM systems.