Revealing sensitive information in personal interviews: Is self-disclosure easier with humans or avatars and under what conditions?

Despite the many advantages of computer-assisted data collection, it is unclear if, when, or under what conditions embodied conversational agents (i.e., ECA, virtual humans) can replace human interviewers to collect personal information in interviews, especially for topics that might be regarded as 'sensitive'. This paper presents results from an exploratory study designed to investigate how topic sensitivity affects individuals' preference to disclose to a human or an ECA interviewer. A convenience sample of 203 undergraduate business students completed a scenario-based survey that asked them to rate the sensitivity of various interview topics and indicate their preference to disclose such sensitive information to human or ECA interviewers. Open-ended questions revealed factors behind preferences for interviewer choice. Findings show a preference for ECAs when topics are highly sensitive and more likely to evoke negative self-admissions. For topics rated low in sensitivity or more likely to evoke positive self-admissions, human interviewers are preferred. Specifically, participants stated that they would feel more comfortable discussing sensitive topics with an ECA interviewer because it could not judge them. This indicates that the evaluative capability of the interviewer plays a factor in the amount of sensitive information elicited from interviewees. Overall, results contribute to an understanding of when and why ECA interviewers can effectively replace human interviewers.