Effects of Gender Stereotypes on Trust and Likability in Spoken Human-Robot Interaction

By Matthias Kraus, Johannes Maria Kraus, Martin R. K. Baumann, Wolfgang Minker. 2018

44 citations recorded by the source corpus.

As robots enter more and more areas of everyday life, it becomes necessary for them to interact in an understandable and trustworthy way. In many regards this requires a human-like interaction pattern. This research investigates the influence of gender stereotypes on trust and likability of humanoid robots. In this endeavor, explicit (name and voice) and implicit gender (personality) of robots have been manipulated along with the stereotypicality of a task. 40 participants interacted with a NAO robot to gain feedback on a task they were working on and rated the perception of the robot cooperation partner. While no gender stereotypes were found for the explicit gender, implicit gender showed a strong effect on trust and likability in the stereotypical male task. Participants trusted the male robot more and rated it as more reliable and competent than the female personality robot, while the female robot was perceived as more likable. These findings indicate that for gender stereotypes in robot interaction a differentiation between explicit and implicit stereotypical features have to be drawn and that the task context needs consideration. Future research may look into situational variables that drive stereotypification in human-robot interaction.

Subfields: Artificial Intelligence, Control and Systems Engineering, Social Psychology

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In Artificial Intelligence

  1. James Y. Zou
  2. Friederike A. Eyssel
  3. Londa Schiebinger
  4. Sunipa Dev
  5. Weixin Liang
  6. Kai-Wei Chang
  7. Percy Liang

In Control and Systems Engineering

  1. James Y. Zou

In Social Psychology

  1. Friederike A. Eyssel

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