In this paper, I argue that patient preferences concerning human physical attributes associated with race, culture, and gender should be excluded from public healthcare robot design. On one hand, healthcare should be (objective, universal) needs oriented. On the other hand, patient well-being (the aim of healthcare) is, in concrete ways, tied to preferences, as is patient satisfaction (a core WHO value). The shift toward patient-centered healthcare places patient preferences into the spotlight. Accordingly, the design of healthcare technology cannot simply disregard (...) patient preferences, even those which are potentially morally problematic. A method for handling these at the design level is thus imperative. By way of uncontroversial starting points, I argue that the priority of the public healthcare system is the fulfillment of patients’ therapeutic needs, among which certain potentially morally problematic preferences may be counted. There are further ethical considerations, however, which, taken together, suggest that the potential benefits of upholding these preferences are outweighed by the potential harms. (shrink)

The paper demonstrates that social alignment is distinct from value alignment as it is currently understood in the AI safety literature, and argues that social alignment is an important research agenda. Work provides an important example for the argument, since work is a cooperative endeavor, and it is part of the larger manifold of social cooperation. These cooperative aspects of work are individually and socially valuable, and so they must be given a central place when evaluating the impact of AI (...) upon work. Workplace technologies are not simply instruments for achieving productive goals, but ways of mediating interpersonal relations. They are aspects of a cooperative interface i.e. the infrastructure by which we engage cooperative behavior with others. The concept of the cooperative interface suggests two conjectures to foreground in the social alignment agenda, motivated by the experience of algorithmic trading and social robotics: that AI impacts cooperation through its effects on social networks, and through its effects on social norms. (shrink)

Social categorizations regarding gender or age have proven to be relevant in human-robot interaction. Their stereotypical application in the development and implementation of robotics in eldercare is even discussed as a strategy to enhance the acceptance, well-being, and quality of life of older people. This raises serious ethical concerns, e.g., regarding autonomy of and discrimination against users. In this paper, we examine how relevant professional stakeholders perceive and evaluate the use of social categorizations and stereotypes regarding gender and age in (...) robotics for eldercare. Based on 16 semi-structured interviews with representatives from technology development, industry, and nursing science as well as practice, we explore the subjects’ awareness, evaluations, and lines of argument regarding the corresponding moral challenges. Six different approaches of dealing with categorizations and stereotypes regarding gender and age in care robotics for older people are identified: negation, functionalistic relativization, explanation, neutralization, stereotyping, and queering. We discuss the ethical implications of these approaches with regard to professional responsibility and draw conclusions for responsible age tech in pluralistic societies. (shrink)

The increase in the spread of conversational agents urgently requires to tackle the ethical issues linked to their design. In fact, developers frequently include in their products cues that trigger social biases in order to maximize the performance and the quality of human-machine interactions. The present paper discusses whether and to what extent it is ethically sound to intentionally trigger gender biases through the design of virtually embodied conversational agents. After outlining the complex dynamics involving social biases, social robots, and (...) design, we evaluate the ethics of integrating gender cues in conversational agents, analysing four different approaches to the problem. Finally, we suggest which approach in our opinion might have the best chances to reduce the negative effects of biases and discriminatory visions of gender dynamics. (shrink)

The individual physical appearances of robots are considered significant, similar to the way that those of humans are. We investigated whether users prefer robots with male or female physical appearances for use in daily communication situations and whether egalitarian gender role attitudes are related to this preference. One thousand adult men and women aged 20–60 participated in the questionnaire survey. The results of our study showed that in most situations and for most subjects, “males” was not selected and “females” or (...) “neither” was selected. Moreover, the number of respondents who chose “either” was higher than that who chose “female.” Furthermore, we examined the relationship between gender and gender preference and confirmed that the effect of gender on the gender preference for a robot weakened when the human factor was eliminated. In addition, in some situations for android-type robots and in all situations for machine-type robots, equality orientation in gender role attitudes was shown to be higher for people who were not specific about their gender preferences. It is concluded that there is no need to introduce a robot that specifies its gender. Robots with a gender-neutral appearance might be more appropriate for applications requiring complex human–robot interaction and help avoid reproducing a gender bias. (shrink)

We introduce a construct called “humanness sensitivity,” which we define as the ability to recognize uniquely human characteristics. To evaluate the construct, we used a “concurrent study design” to conduct an internet-based study with a convenience sample of 42,063 people from 88 countries (52.4% from the U.S. and Canada).We sought to determine to what extent people could identify subtle characteristics of human behavior, thinking, emotions, and social relationships which currently distinguish humans from non-human entities such as bots. Many people were (...) surprisingly poor at this task, even when asked simple questions about human relationships or anatomy. Participants were best at identifying subtle aspects of human cognition and worst at identifying subtle aspects of human communication. Test scores were good predictors of whether someone was employed and modest predictors of other self-reported criterion measures. We also found that people identifying themselves in marginal societal categories (e.g., in the “other” category for gender or sexual orientation) identified themselves as less human and also scored lower on our test. As computers continue to become more human-like, our study suggests that the vast majority of humankind will likely have great difficulty distinguishing them from people. Can methods be devised for improving this ability? Might humanness sensitivity help people to make such distinctions? Will people who excel at differentiating humans and non-human entities—like the “blade runners” in the 1982 and 2017 feature films—someday hold a special place in society? (shrink)

Nass’ and Reeves’ media equation paradigm within human–computer interaction challenges long-held assumptions about how users approach computers. Given a rudimentary set of cues present in the system’s design, users are said to unconsciously treat computers as genuine interactants—extending rules of politeness, biases and human interactive conventions to machines. Since the results have wide-ranging implications for HCI research methods, interface design and user experiences, researchers are hard-pressed to experimentally verify the paradigm. This paper focuses on the methodology of attributing the necessary (...) social cues to the agent, a core aspect of the experimental design of studies dealing with the media equation. A typology of experimental anthropomorphisms is developed, allowing an assessment of how the differing axiomatic assumptions affect the relevance of the results for an evaluation of the paradigm. The paper concludes with a series of arguments in favour of one particular anthropomorphism type for researching the media equation. (shrink)

Despite facing significant criticism for assigning human-like characteristics to artificial intelligence, phrases like “trustworthy AI” are still commonly used in official documents and ethical guidelines. It is essential to consider why institutions continue to use these phrases, even though they are controversial. This article critically evaluates various reasons for using these terms, including ontological, legal, communicative, and psychological arguments. All these justifications share the common feature of trying to justify the official use of terms like “trustworthy AI” by appealing to (...) the need to reflect pre-existing facts, be it the ontological status, ways of representing AI or legal categories. The article challenges the justifications for these linguistic practices observed in the field of AI ethics and AI science communication. In particular, it takes aim at two main arguments. The first is the notion that ethical discourse can move forward without the need for philosophical clarification, bypassing existing debates. The second justification argues that it’s acceptable to use anthropomorphic terms because they are consistent with the common concepts of AI held by non-experts—exaggerating this time the existing evidence and ignoring the possibility that folk beliefs about AI are not consistent and come closer to semi-propositional beliefs. The article sounds a strong warning against the use of human-centric language when discussing AI, both in terms of principle and the potential consequences. It argues that the use of such terminology risks shaping public opinion in ways that could have negative outcomes. (shrink)

Customers have obvious occupational gender stereotypes for service employees. In recent years, intelligent service robots have been widely used in the hospitality industry and have also been given gender characteristics to attract customers to use them. However, whether and when the usage of gendered service robots is effective remains to be explored. This research focuses on customers' occupational gender stereotypes and the gender of service robots, examining the influences of their consistency on customers' willingness to use service robots through three (...) scenario studies. The findings suggest that: The consistency between occupational gender stereotypes and service robot gender positively affects customers' willingness to use service robots. Performance expectancy and trust are two psychological mechanisms underlying the above effect. In the context of service failures, the consistency backfires and brings negative effects on willingness to use. This research extends the literature on customers' acceptance of anthropomorphized robots from the perspective of gender stereotypes and identifies the mechanisms behind the stereotype consistency effect. For practical implications, hotels should design and deploy gendered robots consistent with corresponding occupational gender stereotypes. (shrink)

Spatial communications are essential to the survival and social interaction of human beings. In science fiction and the near future, robots are supposed to be able to understand spatial languages to collaborate and cooperate with humans. However, it remains unknown whether human speakers regard robots as human-like social partners. In this study, human speakers describe target locations to an imaginary human or robot addressee under various scenarios varying in relative speaker–addressee cognitive burden. Speakers made equivalent perspective choices to human and (...) robot addressees, which consistently shifted according to the relative speaker–addressee cognitive burden. However, speakers’ perspective choice was only significantly correlated to their social skills when the addressees were humans but not robots. These results suggested that people generally assume robots and humans with equal capabilities in understanding spatial descriptions but do not regard robots as human-like social partners. (shrink)