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Because robots are perceived as moral agents, they must behave in accordance with human systems of morality. This responsibility is especially acute for language-capable robots because moral communication is a method for building moral ecosystems. Language capable robots must not only make sure that what they say adheres to moral norms; they must also actively engage in moral communication to regulate and encourage human compliance with those norms. In this work, we describe four experiments (total N =316) across which we systematically evaluate two different moral communication strategies that robots could use to influence human behavior: a norm-based strategy grounded in deontological ethics, and a role-based strategy grounded in role ethics. Specifically, we assess the effectiveness of robots that use these two strategies to encourage human compliance with norms grounded in expectations of behavior associated with certain social roles. Our results suggest two major findings, demonstrating the importance of moral reflection and moral practice for effective moral communication: First, opportunities for reflection on ethical principles may increase the efficacy of robots’ role-based moral language; and second, following robots’ moral language with opportunities for moral practice may facilitate role-based moral cultivation.
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Toward Hybrid Relational-Normative Models of Robot Cognition
Most previous work on enabling robots’ moral competence has used norm-based systems of moral reasoning. However, a number of limitations to norm-based ethical theories have been widely acknowledged. These limitations may be addressed by role-based ethical theories, which have been extensively discussed in the philosophy of technology literature but have received little attention within robotics. My work proposes a hybrid role/norm-based model of robot cognitive processes including moral cognition.
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- PAR ID:
- 10265915
- Date Published:
- Journal Name:
- ACM/IEEE International Conference on Human-Robot Interaction
- Page Range / eLocation ID:
- 568 to 570
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3434074.3446353
