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Learning How to Behave

Moral Competence for Social Robots
  • Bertram F. MalleEmail author
  • Matthias Scheutz
Chapter

Abstract

We describe a theoretical framework and recent research on one key aspect of robot ethics: the development and implementation of a robot’s moral competence. As autonomous machines take on increasingly social roles in human communities, these machines need to have some level of moral competence to ensure safety, acceptance, and justified trust. We review the extensive and complex elements of human moral competence and ask how analogous competences could be implemented in a robot. We propose that moral competence consists of five elements, two constituents (moral norms and moral vocabulary) and three activities (moral judgment, moral action, and moral communication). A robot’s computational representations of social and moral norms is a prerequisite for all three moral activities. However, merely programming in advance the vast network of human norms is impossible, so new computational learning algorithms are needed that allow robots to acquire and update the context-specific and graded norms relevant to their domain of deployment. Moral vocabulary is needed primarily for moral communication, which expresses moral judgments of others’ violations and explains one’s own moral violations – to justify them, apologize, or declare intentions to do better. Current robots have at best rudimentary moral competence, but with improved learning and reasoning they may begin to show the kinds of capacities that humans will expect of future social robots.

Keywords

Norms Robot ethics Machine morality Moral action Moral judgment Machine learning Explanations 

Notes

Acknowledgments

This project was supported by a grant from the Office of Naval Research (ONR), No. N00014-13-1-0269, and from the Defense Advanced Research Projects Agency (DARPA), SIMPLEX 14-46-FP-097. The opinions expressed here are our own and do not necessarily reflect the views of ONR or DARPA.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Cognitive, Linguistic, and Psychological SciencesBrown UniversityProvidenceVereinigte Staaten
  2. 2.Department of Computer ScienceTufts UniversityMedfordVereinigte Staaten

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