Paolo Euron's "Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots" lays out a vision for appreciating sex robots in aesthetic terms, centering the concept of "beauty" as a measure of what they can inspire culturally and existentially. In these comments I turn toward the field of human-robot interaction and the ethical challenges that inhabit the core of such an aesthetic turn.

Explainability has emerged as a critical AI research objective, but the breadth of proposed methods and application domains suggest that criteria for explanation vary greatly. In particular, what counts as a good explanation, and what kinds of explanation are computationally feasible, has become trickier in light of oqaque “black box” systems such as deep neural networks. Explanation in such cases has drifted from what many philosophers stipulated as having to involve deductive and causal principles to mere “interpretation,” which approximates what happened in the target system to varying degrees. However, such post hoc constructed rationalizations are highly problematic for social robots that operate interactively in spaces shared with humans. For in such social contexts, explanations of behavior, and, in particular, justifications for violations of expected behavior, should make reference to socially accepted principles and norms. In this article, we show how a social robot’s actions can face explanatory demands for how it came to act on its decision, what goals, tasks, or purposes its design had those actions pursue and what norms or social constraints the system recognizes in the course of its action. As a result, we argue that explanations for social robots will need to be accurate representations of the system’s operation along causal, purposive, and justificatory lines. These explanations will need to generate appropriate references to principles and norms—explanations based on mere “interpretability” will ultimately fail to connect the robot’s behaviors to its appropriate determinants. We then lay out the foundations for a cognitive robotic architecture for HRI, together with particular component algorithms, for generating explanations and engaging in justificatory dialogues with human interactants. Such explanations track the robot’s actual decision-making and behavior, which themselves are determined by normative principles the robot can describe and use for justifications.

HRI researchers have made major strides in developing robotic architectures that are capable of reading a limited set of social cues and producing behaviors that enhance their likeability and feeling of comfort amongst humans. However, the cues in these models are fairly direct and the interactions largely dyadic. To capture the normative qualities of interaction more robustly, we propose “consent” as a distinct, critical area for HRI research. Convening important insights in existing HRI work around topics like touch, proxemics, gaze, and moral norms, the notion of consent reveals key expectations that can shape how a robot acts in social spaces. Consent need not be limited to just an explicit permission given in ethically charged or normatively risky scenarios. Instead, it is a richer notion, one that covers even implicit acquiescence in scenarios that otherwise seem normatively neutral. By sorting various kinds of consent through social and legal doctrine, we delineate empirical and technical questions to meet consent challenges faced in major application domains and robotic roles. Attention to consent could show, for example, how extraordinary, norm-violating actions can be justified by agents and accepted by those around them. We argue that operationalizing ideas from legal scholarship can better guide how robotic systems might cultivate and sustain proper forms of consent.

The challenge of training AI systems to perform responsibly and beneficially has inspired different approaches for teaching a system what people want and how it is acceptable to attain that in the world. In this paper we compare work in reinforcement learning, in particular inverse reinforcement learning, with our norm inference approach. We test those two systems and present results. Using the idea of the "intentional stance", we explain how a norm inference approach can work even when another agent is acting strictly according to reward functions. In this way norm inference presents itself as a promising, more explicitly accountable approach with which to design AI systems from the start.

The complex role of touch is an increasingly appreciated horizon for HRI research. The explicit and implicit registers of touch, both human-to-robot and robot-to-human, have opened up pressing questions in design and HRI ethics about embodiment, communication, care, and human affection. In this paper we present results of an MTurk survey about robot-initiated touch in a social context. We examine how a positive or negative attitude from the robot, as well as whether the robot touches an interactant, affects how a robot is judged as a worker and teammate. Our findings confirm previous empirical support for the idea of touch as enhancing social appraisals of a robot, though the extent of that positive tactile role was complicated and tempered by the survey responses» gender effects.

HRI research has yielded intriguing empirical results connected to ethics and how we act in social contexts with robots, even though much of this work has focused on task-based, one-on-one interaction. In this paper, we point to the need to investigate a wider range of ethically relevant dynamics that interaction with robots carries with it -- individually and in groups, with a single robot or more. We specifically examine three areas: 1) the primacy and implicit dynamics of bodily perception, 2) the competing interests at work in a single robot-human interaction, and 3) the social intricacy of multiple agents -- robots and humans -- communicating and making decisions. While these areas are not exhaustive by any means, we find they yield concrete directions for how HRI can contribute to a widening, intensifying set of ethical debates with critical empirical insight, starting to explore more of the ethical landscape in HRI.

Sex robots are gaining a remarkable amount of attention in current discussions about technology and the future of human relationships. To help understand what kinds of relationships people will have with these robots, empirical data about people's views of sex robots is needed. We report the results of the first systematic survey that asks about the appropriateness and value of sex robots, acceptable forms they can take, and the degree to which using them counts as sex. The results show a consistent difference in the uses for which women and men found sex robots to be appropriate, with women less and men more inclined to consider them socially useful. We also found convergences on what sex robots are like and how sex with them is to be classified, suggesting that larger views about relationships and society, not just understandings of sex robots themselves, should be a matter for more research and thus frame future work on the ethics of sex robots.

Moral norms play an essential role in regulating human interaction. With the growing sophistication and proliferation of robots, it is important to understand how ordinary people apply moral norms to robot agents and make moral judgments about their behavior. We report the first comparison of people's moral judgments (of permissibility, wrongness, and blame) about human and robot agents. Two online experiments (total N = 316) found that robots, compared with human agents, were more strongly expected to take an action that sacrifices one person for the good of many (a "utilitarian" choice), and they were blamed more than their human counterparts when they did not make that choice. Though the utilitarian sacrifice was generally seen as permissible for human agents, they were blamed more for choosing this option than for doing nothing. These results provide a first step toward a new field of Moral HRI, which is well placed to help guide the design of social robots.