Robot Career Fair: An Exploratory Evaluation of Anthropomorphic Robots in Various Career Categories

Abstract

Robots are being used in a host of different work environments currently. However, to date there has been very little broad exploration into the designs of systems and how that affects users’ perception of fit for the robots in different job categories. In the present experiment we showed participants images of 252 robots and asked them to make assignments of the robots into 16 potential job categories taken from the U.S. Department of Labor. The robots’ overall human likeness, as well as four contributory components of anthropomorphism were used to predict job category assignment. Results indicate that participants expect higher levels of anthropomorphism in jobs with more direct human interactions (such as education and hospitality), whereas they expect minimal levels in jobs with less human interaction (e.g. agriculture and architecture). Results also indicate that there is more nuance required for these judgments than general human likeness.