Judging AI at Work: How Gender, Job Role, and Task Type Shape Beliefs about AI Use

• In Event: Organizations, Occupations, and Work Section Roundtables
  In Refereed Roundtable (90 minute): Table 17: Discrimination Contexts

Abstract

The rapid adoption of artificial intelligence (AI) in the workplace has raised new questions about how workers who use AI are evaluated, yet little research has examined how perceptions of AI use vary by worker characteristics and occupational context. Drawing on theories of gender inequality and occupational segregation, this study uses a survey experiment to examine how employee gender, job role, and type of AI use shape evaluations of workers. Respondents are randomly assigned to a mock performance review that varies gender (man or woman), job role (IT manager or social media manager), and AI use (administrative tasks, generative tasks, or no AI use), and then asked to assess the employee’s competence, motivation, professional ethics, promotion likelihood, and expected salary. Pretest results suggest that AI use is evaluated through gendered and occupational stereotypes: men in male-dominated roles who use AI for generative tasks are viewed as more deserving of higher pay and promotion, while women in IT—particularly those who use AI for generative tasks or do not use AI—are more likely to be perceived as gaining unfair advantages. These findings suggest that AI adoption is not perceived as a neutral efficiency tool but may instead reproduce or exacerbate existing workplace inequalities.

Authors

• Maureen Cowhey, University of California-Los Angeles

• Natasha Quadlin, UCLA