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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. 

To enable robots to exert positive moral influence, we need to understand the impacts of robots’ moral communications, the ways robots can phrase their moral language to be most clear and persuasive, and the ways that these factors interact. Previous work has suggested, for example, that for certain types of robot moral interventions to be successful (i.e., moral interventions grounded in particular ethical frameworks), those interventions may need to be followed by opportunities for moral reflection, during which humans can critically engage with not only the contents of the robot’s moral language, but also with the way that moral language connects with their social-relational ontology and broader moral ecosystem. We conceptually replicate this prior work (N=119) using a design that more precisely manipulates moral ref lection. Our results confirm that opportunities for moral reflection are indeed critical to the success of robotic moral interventions—regardless of the ethical framework in which those interventions are grounded. 

Significant segments of the HRI literature rely on or promote the ability to reason about human identity characteristics, including age, gender, and cultural background. However, attempting to handle identity characteristics raises a number of critical ethical concerns, especially given the spatiotemporal dynamics of these characteristics. In this paper I question whether human identity characteristics can and should be represented, recognized, or reasoned about by robots, with special attention paid to the construct of race, due to its relative lack of consideration within the HRI community. As I will argue, while there are a number of well-warranted reasons why HRI researchers might want to enable robotic consideration of identity characteristics, these reasons are outweighed by a number of key ontological, perceptual, and deployment-oriented concerns. This argument raises troubling questions as to whether robots should even be able to understand or generate descriptions of people, and how they would do so while avoiding these ethical concerns. Finally, I conclude with a discussion of what this means for the HRI community, in terms of both algorithm and robot design, and speculate as to possible paths forward. 

Deployed social robots are increasingly relying on wakeword-based interaction, where interactions are human-initiated by a wakeword like “Hey Jibo”. While wakewords help to increase speech recognition accuracy and ensure privacy, there is concern that wakeword-driven interaction could encourage impolite behavior because wakeword-driven speech is typically phrased as commands. To address these concerns, companies have sought to use wake- word design to encourage interactant politeness, through wakewords like “⟨Name⟩, please”. But while this solution is intended to encourage people to use more “polite words”, researchers have found that these wakeword designs actually decrease interactant politeness in text-based communication, and that other wakeword designs could better encourage politeness by priming users to use Indirect Speech Acts. Yet there has been no previous research to directly compare these wakewords designs in in-person, voice-based human-robot interaction experiments, and previous in-person HRI studies could not effectively study carryover of wakeword-driven politeness and impoliteness into human-human interactions. In this work, we conceptually reproduced these previous studies (n=69) to assess how the wakewords “Hey ⟨Name⟩”, “Excuse me ⟨Name⟩”, and “⟨Name⟩, please” impact robot-directed and human-directed politeness. Our results demonstrate the ways that different types of linguistic priming interact in nuanced ways to induce different types of robot-directed and human-directed politeness. 

To enable robots to exert positive moral influence, we need to understand the impacts of robots’ moral communications, the ways robots can phrase their moral language to be most clear and persuasive, and the ways that these factors interact. Previous work has suggested, for example, that for certain types of robot moral interventions to be successful (i.e., moral interventions grounded in particular ethical frameworks), those interventions may need to be followed by opportunities for moral reflection, during which humans can critically engage with not only the contents of the robot’s moral language, but also with the way that moral language connects with their social-relational ontology and broader moral ecosystem. We conceptually replicate this prior work (N =119) using a design that more precisely manipulates moral reflection. Our results confirm that opportunities for moral reflection are indeed critical to the success of robotic moral interventions—regardless of the ethical framework in which those interventions are grounded. 

For enhanced performance and privacy, companies deploying voice-activated technologies such as virtual assistants and robots are increasingly tending toward designs in which technologies only begin attending to speech once a specified wakeword is heard. Due to concerns that interactions with such technologies could lead users, especially children, to develop impolite habits, some companies have begun to develop use modes in which interactants are required to use ostensibly polite wakewords such as " Please''. In this paper, we argue that these ``please-centering'' wakewords are likely to backfire and actually discourage polite interactions due to the particular types of lexical and syntactic priming induced by those wakewords. We then present the results of a human-subject experiment (n=90) that validates those claims. 

Robots are entering various domains of human societies, potentially unfolding more opportunities for people to perceive robots as social agents. We expect that having robots in proximity would create unique social learning situations where humans spontaneously observe and imitate robots’ behaviors. At times, these occurrences of humans’ imitating robot behaviors may result in a spread of unsafe or unethical behaviors among humans. For responsible robot designing, therefore, we argue that it is essential to understand physical and psychological triggers of social learning in robot design. Grounded in the existing literature of social learning and the uncanny valley theories, we discuss the human-likeness of robot appearance and affective responses associated with robot appearance as likely factors that either facilitate or deter social learning. We propose practical considerations for social learning and robot design.