Diversity Informatics: Reducing Racial and Gender Bias with Virtual Agents

Abstract

Job advertisements in white male-dominated organizations are often biased in ways that discourage female and minority candidates from applying. We explored the use of a female African American virtual agent who provides a first-person reaction to a biased job advertisement, providing an impassioned, vivid description of her feelings about the advertisement, the position, and the organization offering the job, and how the position---as described---would impact her life were she to take the job. We evaluate the impact interactions with this agent have on the effort study participants invest in editing the job advertisement following their interaction with the agent, compared to reading a page of standard educational text on diversity in hiring. Participants who interacted with the agent spent significantly more effort correcting the job ad, as measured both by the number of edit operations and the number of biased phrases removed, compared to participants in the control condition. Implications and a future research agenda for increasing diversity using virtual agents are presented.