Beyond bias

: exploring gender expectations for AI leadership

Abstract

With the rapid advancement of Artificial Intelligence (AI) tools, the implementation of AI in hierarchical roles is no longer a futuristic scenario, as organizations are beginning to incorporate AI robots into leadership positions. These robots typically possess a human-like appearance, being anthropomorphized and often gendered as female or male. The dissertation aims to investigate whether gender stereotypes, as evidenced in existing literature on gender and leadership, are applied onto potential AI leaders. Addressing this research gap, a survey was conducted in an experimental manner with a sample size of N = 502. The study particularly explores whether the acceptance of an AI leader is influenced by the perceived gender of the AI leader and the workplace setting, while observing the mediator variables communality, agency, trust, and competence. For analysis, partial structured equation modeling (PLS-SEM) on Smart PLS was utilized. The results indicate that the gender of the AI does not significantly influence its acceptance; rather, acceptance is influenced by mediator variables. This thesis marks a crucial step in addressing a research gap in a burgeoning field, offering a positive outlook for future studies in this area.

Date of Award	2 May 2024
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Filipa de Almeida (Supervisor)