Artificial emotional intelligence beyond East and West

White, Daniel; Katsuno, Hirofumi Article Artificial emotional intelligence beyond East and West Internet Policy Review Provided in Cooperation with: Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin Suggested Citation: White, Daniel; Katsuno, Hirofumi (2022) : Artificial emotional intelligence beyond East and West, Internet Policy Review, ISSN 2197-6775, Alexander von Humboldt Institute for Internet and Society, Berlin, Vol. 11, Iss. 1, pp. 1-17, https://doi.org/10.14763/2022.1.1618 This Version is available at: https://hdl.handle.net/10419/254268 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/de/legalcode Volume 11 | Artificial emotional intelligence beyond East and West Daniel White University of Cambridge Hirofumi Katsuno Doshisha University DOI: https://doi.org/10.14763/2022.1.1618 Published: 11 February 2022 Received: 17 September 2021 Accepted: 12 December 2022 Funding: Support for this research has been provided by Freie Universität Berlin, Doshisha University, the University of Cambridge, and a JST RISTEX Grant (number JPMJRX19H5). Competing Interests: The author has declared that no competing interests exist that have influenced the text. Licence: This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License (Germany) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/3.0/de/deed.en Copyright remains with the author(s). Citation: White, D. & Katsuno, H. (2022). Artificial emotional intelligence beyond East and West. Internet Policy Review, 11(1). https://doi.org/10.14763/2022.1.1618 Keywords: Artificial intelligence, Affective computing, Emotion, Engineering Abstract: Artificial emotional intelligence refers to technologies that perform, recognise, or record affective states. More than merely a technological function, however, it is also a social process whereby cultural assumptions about what emotions are and how they are made are translated into composites of code, software, and mechanical platforms that operationalise certain models of emotion over others. This essay illustrates how aspects of cultural difference are both incorporated and elided in projects that equip machines with emotional intelligence. It does so by comparing the field of affective computing, which emerged in the North-Atlantic in the 1990s, with kansei (affective) engineering, which developed in Japan in the 1980s. It then leverages this comparison to argue for more diverse applications of the culture concept in both the development and critique of systems with artificial emotional intelligence. Issue 1 This article belongs to Concepts of the digital society, a special section of Internet Policy Review guest-edited by Christian Katzenbach and Thomas Christian Bächle. 1. INTRODUCTION There is a robot in Japan called LOVOT that is designed, so its makers advertise, ‘to be loved by you’ (Groove X, 2019, n.p.). To facilitate this, its engineers at its parent company Groove X have equipped LOVOT with trademarked ‘emotional robotics’, which refers to ‘a robot technology that was created to stir people’s feelings, through the ways that the robots look, feel and behave’ (Groove X, 2019, n.p.). LOVOT is small, soft, furry, and is designed to be about the same weight and temperature of a human baby. It can roll around on wheels that fold into its body when it is picked up and held. Tactile sensors on its body register human-robot interactions that are recorded in a ‘Diary’ accessible by a smartphone application for other registered users—such as a curious parent, for example—to track. Through a ‘horn’ on its head, which hosts a video camera and processor, LOVOT can map its surroundings, navigate obstacles, and record up to 1,000 distinct human faces, as well as facial expressions which signal different emotions—although this function has not yet been activated according to the authors’ conversations with company staff. Based on interactions with users, the robot can also assign values to people and rank them according to a hierarchy of ‘preference’: those users offering the most positive interactions, such as through regular displays of tactile affection, receive the highest rating. Through these mechanisms and others, LOVOT exemplifies what many robot researchers, engineers, and marketers call ‘artificial emotional intelligence’. What do emotions become when transcribed into digital platforms? Evaluating the emergence of new technologies, algorithms, and digital platforms incorporated into machines like LOVOT requires tracing the processes by which the definitions, meanings, and significance of emotional experience change when emotions are converted into meanings that can be processed in digital form. Adding a critical perspective to this process is important given the degree to which such technologies can mislead users on the accuracy and purported universality of their emotion-recognition abilities. Although the quantification of emotion has a long history (Bollmer, 2019; Crawford, 2021; Lupton, 2016; Wilson, 2010), more recent are practices of digitalising emotion that combine smart cameras, social robots, wearable devices, and other technologies with machine learning and algorithmic forms of analysis. This approach to interpreting emotion through quantitative metrics, 2 Internet Policy Review 11(1) | 2022 combined with increased computing potential, has made the concept of ‘artificial emotional intelligence’ into a powerful sociotechnical tool. It is for this reason that we aim to contribute considerations of cultural diversity that can make the term into a powerful critical tool as well. ‘Artificial emotional intelligence’ is an umbrella concept used by digital technology developers and researchers to designate technologies estimated by engineers or users to have emotional capacities. Defined by Andrew McStay (2018, p. 3), ‘artificial emotional intelligence’ can be understood by the ‘capacity to see, read, listen, feel, classify and learn about emotional life’. It can incorporate ‘reading words and images, seeing and sensing facial expressions, gaze direction, gestures and voice. It also encompasses machines feeling our heart rate, body temperature, respiration and the electrical properties of our skin, among other bodily behaviors’ (McStay, 2018, p. 3). In this essay we build on this definition as a constructive critical starting point. Most importantly, because different groups of people disagree on what emotions are, how they work, why they matter, and even how they physically feel, we argue that there is good reason to incorporate into this definition a culturally diverse perspective in order to better evaluate the significance as well as the threats posed by the rise of technologies with emotional capacities. This task is even more critical when one considers how different groups of people approach not only emotional experiences in various ways but also the very technologies, such as LOVOT, that mediate them. For this reason, we focus our attention on some ways that emotion has in different cultural contexts become differently digitalised through emerging technologies equipped with artificial emotional intelligence. We do so not to establish a fixed definition of the term ‘artificial emotional intelligence’ that can be uniformly or universally applied across comparable contexts. However, neither do we propose to substitute for this universal approach an equally simple one of cultural differences, such as those between a so-called ‘East’ and ‘West’. Rather, we aim to broaden the meaning and critical acuity of the term ‘artificial emotional intelligence’ to better encapsulate the diversity and complexity of the cultural conditions under which emotion and technology are increasingly combined today. Writing from the perspective of cultural anthropology and media studies, we argue that while economic structures underlying the development of emotional technologies incentivise engineers to build universal models of emotion recognition, the ethnographic record demonstrates a diversity of emotional experience that proves more difficult to capture through code. In the following sections we draw out the importance of this dialectic between 3 White, Katsuno universal and particular models of emotion by, first, summarising the recent historical context for efforts in measuring emotion in digital landscapes (section 2); second, describing the rise of the field of affective computing that has come to dominate artificial emotion research largely in anglophone cultural contexts (section 3); third, considering alternative approaches to artificial emotional intelligence, such as those from Japan, where we conduct ethnographic fieldwork on emotional robotics and affective engineering (kansei kōgaku) (section 4); and finally, advocating for the diversification of the concept of ‘culture’ itself so that developers might better incorporate aspects of cultural diversity into emotional technology design and researchers might further refine their critique of emotional technology development (section 5). 2. KEY CONCEPTS AND HISTORICAL CONTEXT The question of whether manufactured objects can perform, understand, or even ‘have’ emotions is an old theme in the diverse literary and philosophical narratives of artificial intelligence (see Cave et al., 2020). Many of these narratives draw heavily from even earlier efforts to formalise a psychological science of emotion. In the late nineteenth century, for example, from naturalists such as Darwin to early neurologists like Guillaume Duchenne, a broad range of researchers combined an analytical view of an evolving natural science with emerging media technologies. Using tools such as illustrated and, later, photographic ‘books of faces’ (Bollmer, 2019), European and North American scientists applied an experimental lens to interpret the philosophical puzzle of the body’s affective states. These precedents established a sociotechnical legitimacy around using the face and body to decode emotion. With later technological developments that enabled digital data collection, such decoding processes could be easily incorporated into an accelerating technological science of emotion detection. This historical process highlights the increasing importance today of rapidly advancing practices of datafication and digitisation—concepts that are critical to our analysis and covered earlier in this series. According to Viktor Mayer-Schönberger and Kenneth Cukier (2013, p. 78), ‘datafication’ refers to the process of putting data into a ‘quantified format so it can be tabulated and analyzed’. This refers as much, the authors discuss (2013, pp. 76–80), to US Navy officer Matthew Maury’s catalogue of sailing data in the mid-nineteenth century that radically rationalised marine navigation as it does to engineer Koshimizu Shigeomi, who collected 360 points of pressure data from car seat sensors in order to produce a digital ID code for individual drivers. 4 Internet Policy Review 11(1) | 2022 The clarity and simplicity of this definition of datafication is helpful, but it also leaves out important social dimensions critical to the datafication of emotion in particular. As Ulises Mejias and Nick Couldry explain, ‘datafication has major social consequences’ (2019, p. 1), and incorporates ‘the wider transformation of human life so that its elements can be a continual source of data’ (p. 2). Built into technical processes of datafication, then, especially since the rise in scientific, corporate, and then home computing beginning most prominently in the 1950s, are social processes by which data is manufactured out of human interactions both with other humans and with emerging technologies like LOVOT. Even more importantly, these processes expand and accelerate practices of technological ‘enclosure’ (see Roquet, 2020) by which increasing aspects of daily life are rendered codable for machines. Also accelerating processes of datafication are those of ‘digitisation’, which ‘turbocharge datafication’ by ‘turning analog information into computer-readable format’ (Mayer-Schönberger and Cukier, 2013, p. 85). Combined with the rapid acceleration of computer processing, mobile computing, and machine learning, digitisation incorporates datafication’s social emphasis on quantification with material technologies that automate processes of data processing and analysis. The at once social and technological processes of datafication and digitisation enabled early work in computing and control systems that has come to be associated with the term ‘artificial intelligence’, and whose origins are often ascribed to the ‘The Dartmouth Summer Research Project on Artificial Intelligence’ in New Hampshire in 1956.1 The ascension of the term ‘artificial intelligence’ in English has foregrounded cognition in processes of representing human intelligence in machines, reinforced by the popularisation of Alan Turing’s 1950 paper ‘Computing Machinery and Intelligence’, from which is derived an enduring legacy that associates the measure of intelligence on a measurement of ‘thought’. However, other scientific traditions have just as readily proposed ‘emotion’ as an equally important marker of intelligence. For example, when scientific literature on artificial intelligence began to enter Japan in the 1960s, because Japanese terms for ‘intelligence’ (chinō) and ‘mind’ 1. The first use of the term ‘artificial intelligence’ is attributed to the American computer scientist John McCarthy. In 1955 McCarthy and colleagues proposed to host a two-month workshop that tested the hypothesis that nearly all aspects of learning and intelligence could be simulated in machines. This workshop became the Dartmouth Summer Research Project, which took place in 1956. Although the term was not initially embraced by all of the workshop’s participants, it was increasingly accepted and advanced by many of its leading researchers working at MIT, such as Marvin Minsky, where AI research became prominent (Cave et al., forthcoming 2022). 5 White, Katsuno (kokoro) refer symbolically to the heart as much as to the brain, the task of representing intelligence in machines in Japan had long been entangled with representing emotion (Katsuno and White, forthcoming 2022).2 Accordingly, because emotion was understood to be an embodied capacity, intelligence itself was understood to require a body to best represent it. This is why engagements with artificial intelligence in Japan have largely relied on concepts of ‘embodied intelligence’ and ‘embodied cognition’ (Robertson, 2018, pp. 82–86), and often gone hand in hand with the development of humanoid robotics. When anglophone research began more explicitly engaging emotion with works like Rosalind Picard’s Affective Computing (1997) and Marvin Minsky’s The Emotion Machine (2006), coming arguably much later than in Japan,3 they were initially seen as exceptional and even marginal perspectives on intelligence. Such cultural differences in the approach to representing intelligence in machines suggests the important role that social context plays in the production not only of technologies that are manufacturing and collecting new forms of emotional data but also of the theories of emotion on which those technologies rely. To draw out the significance of certain cultural differences in defining artificial emotional intelligence, in the next two sections we compare the formation of the field of affective computing, originating mainly in North America and Europe, with approaches to emotional robotics and affective engineering (kansei kōgaku), which emerged in Japan. To reiterate, however, we set up this comparison in the next sections between a so-called ‘West’ and ‘East’ that is all-too-common in cultural scholarship on robotics in order to seek ways to better think beyond it in the final sections. 3. AFFECTIVE COMPUTING IN NORTH-ATLANTIC SCHOLARSHIP In a 1995 working paper and later in a discipline-establishing book titled Affective Computing in 1997, the MIT computer scientist and entrepreneur Rosalind Picard argued that programmers, coders, and computer engineers need to ask questions about the relationship between emotions and computing. She proposed calling this field affective computing and summarised its principal concerns as the investi2. Much of this early translation was stimulated by the mathematician Norbert Wiener’s visit to Japan in 1956, which attracted significant press in Japan, where Wiener’s work still has enormous influence. 3. As early as the 1980s the entertainment company NAMCO had sponsored a project run by Japan’s Foundation for Advancement of International Science (FAIS) that explored ‘the world of emotional robots’ (jōcho robotto no sekai) and sought to define how affect and emotion should be treated within human-robot relationships (Ōhashi et al., 1985). 6 Internet Policy Review 11(1) | 2022 gation and engineering of computers and software that can ‘recognize’ human emotion, can ‘express’ and perform emotion, and that can in some way even ‘“have” emotion’ (Picard, 1995, p. 1). In featuring these three objectives, Picard rendered the question of emotion as one that computer scientists could elucidate with their particular tool kit of coding, natural language processing, and automation—even to the extent of answering philosophical questions about the very nature of emotion. In combination with the increasing ubiquity of social media platforms, as well as with wearable and mobile devices that offer a variety of physiological tracking capacities, computer scientists have recognised in emotional data an interest among manufacturers in potential profit generation and an appeal among consumers for tracking, self-development, and self-care. Accordingly, within the field of emotion research, there is an enormous incentive in terms of both monetisation and professional development to build systems that automate the detection of human emotion. Such systems are applied to various uses today, albeit while also raising questions about their accuracy and legitimacy. For example, smart wristbands made by Picard’s company Empatica are designed to measure levels of anxiety and other affective patterns in the body through indicators like skin conductance and heart rate variability, as well as help ‘detect a possible tonic-clonic seizure’ for those who have epilepsy (Empatica, 2021). A software application by Affectiva called Affdex (another spin-off company from Picard’s lab) purports to record emotions through facial expressions. If advertisers want to know how consumers feel about the video content companies are producing, they can collect this data by willing participants. Finally and most controversially, similar interpretive methods of facial expression recognition have been employed by security personnel, such as in American airports in the wake of the 9/11 attacks on the World Trade Center (Crawford, 2021, p. 170). A version of facial expression and physiological recognition technology called ‘I-BORDER-CTRL’, made by European Dynamics, has also been tested at EU border gates to offer ‘lie-detecting avatars’ and ‘advanced analytics’ for ‘risk-based management’. Project summaries state that this ‘unique approach to “deception detection” analyzes the micro-gestures of travellers to figure out if the interviewee is lying’ (Boffey, 2018; European Dynamics, 2021; also see König, 2016 and Hall and Clapton, 2021). Despite the impression projected by these technologies that they provide objective measures of internal feeling states, it is important to clarify that such technologies record only visible signs of such states. As psychologists like Lisa Feldman Barrett have made clear (2017; also see Le Mau, Hoemann and Lyons et al., 2021), these 7 White, Katsuno are not nearly the same thing. Consequently, critics argue that such technologies can mislead users on the accuracy of these platforms and suggest a degree of authority and certainty not reflected in psychological literature. Such conditions engender multiple tensions among marketers, developers, and researchers between those who are encouraged by the presumed but misleading universality of emotional AI platforms and those who aim to deliver tools to support the cultivation of emotional intelligence that are also sensitive to cultural diversity. Sustaining this tension is a social practice of modelling emotion in machines whereby certain psychological theories of emotion most conducive to quantifiable—but not always reliable—emotion interpretation are selected over others. In this process, programmers and roboticists interested in building a machine capable of registering feeling states must start with a psychological model of how emotions work. Computer scientists Ruth Aylett and Ana Paiva (2012, p. 253) artfully summarise the technological, ethical, and social implications of this challenge: In order to implement any model on a computer, the model itself must be sufficiently specific. From this perspective, many psychological models are not usable as they stand, but must be operationalized. Qualitative relationships must be quantified...Thus, when computer scientists select models from psychology, they tend to favour those that are already sufficiently specific or that can be made so relatively easily. The social result of efforts to technically model emotion in software is that engineers gravitate toward those models that tend to be easily implementable in autonomous systems and generative of quantitative data. A popular example of such a universal model is that of psychologist Paul Ekman’s theory of ‘basic emotions’. In over forty years of research on the expressions of the emotions, Ekman (1999) developed a model of six basic emotions that he considered universally identifiable in facial expressions across cultures. Even more importantly, he also developed a rigorous coding system to render emotions uniformly readable. In conjunction with Wallace Friesen, and drawing on the work of anatomist Carl-Herman Hjortsjö, Ekman and Friesen (1978) designed the Facial Action Coding System (FACS). With its second published edition over 500 pages in length and outlining specific facial Action Units (AU) and exercises to recognise them, FACS provides programmers with a systematic means to code facial expression in a way that is easily implemented in software. It is thus this technical system, and in turn Ekman’s model of emotions more generally, that has become the fundamental basis 8 Internet Policy Review 11(1) | 2022 Japanese Studies, 31(1), 93–109. https://doi.org/10.1080/10371397.2011.560259 Katsuno, H. (2015). Branding humanoid Japan. In G. Kirsch, D. P. Martinez, & M. White (Eds.), Assembling Japan: Modernity, technology and global culture (pp. 205–230). Peter Lang. Katsuno, H., & White, D. (2022). AI in Japanese. Chapter section of The meanings of AI: A crosscultural comparison. In K. Dihal & S. Cave (Eds.), Imagining AI: How the world sees intelligent machines (forthcoming). Oxford University Press. Kobayashi, H., Hara, F., Uchida, G., & Ōno, M. (1994). Akuteibu hyūman intafēsu (AHI) no tame no kao robotto no kenkyū (Study of face robot for active human interface: Mechanisms of face robot and facial expressions of 6 basic emotions). Robotto Gakkaishi (Journal of the Robotics Society of Japan), 12(1), 155–163. https://doi.org/10.7210/jrsj.12.155 König, M. (2016). The borders, they are a-changin’! The emergence of socio-digital borders in the EU. Internet Policy Review, 5(1). https://doi.org/10.14763/2016.1.403 Kuwako, T. (2001). Kansei no tetsugaku (Philosophy of kansei). NHK books. Le Mau, T., Hoemann, K., Lyons, S. H., Fugate, J. M. B., Brown, E. N., Gendron, M., & Barrett, L. F. (2021). Professional actors demonstrate variability, not stereotypical expressions, when portraying emotional states in photographs. Nature Communications, 12(1), 5037. https://doi.org/10.1038/s414 67-021-25352-6 Lévy, P. (2013). Beyond kansei engineering: The emancipation of kansei design. International Journal of Design, 7(2), 83–94. Lupton, D. (2016). The quantified self: A sociology of self-tracking. Polity. Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. McStay, A. (2018). Emotional AI: The Rise of Empathic Media. SAGE Publications Ltd. https://doi.org/1 0.4135/9781526451293 Mejias, U. A., & Couldry, N. (2019). Datafication. Internet Policy Review, 8(4). https://doi.org/10.1476 3/2019.4.1428 Minsky, M. (2007). The emotion machine: Commensense thinking, artificial intelligence, and the future of the human mind (1. Simon & Schuster trade paperback ed). Simon & Schuster. Nagamachi, M. (1989). Kansei kōgaku (Kansei engineering). Kaibundō Shuppan. Nagamachi, M. (Ed.). (1990). Kaiteki kagaku: Ningengawa kara mita shōhinzukuri e (Science of comfort: Toward a creation of production from a human-centered perspective). Kaibundō Shuppan. Nagamachi, M. (1994). Kansei kōgaku to wa (What is sense engineering? Kansei to seni seihin tokushū (Sense and soft products special issue), 468–472. Nagamachi, M. (1995). Kansei Engineering: A new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics, 15(1), 3–11. https://doi.org/10.1 016/0169-8141(94)00052-5 Nagasawa, S. (1998). Kansei kōgaku no kiso to genjō (The fundamentals and current status of kansei engineering. Japan Society for Fuzzy Theory and Systems, 10(4), 647–661. https://doi.org/10.3 156/jfuzzy.10.4_67 15 White, Katsuno Nakamura, T. (2013). The scope of aesthetics for comparative aesthetics: An examination of kanseigaku in Japan. Aesthetics, 23(1), 135–154. Ōhashi, T., Oda, S., Hidaka, T., & Murakami, Y. (Eds.). (1985). Jōcho robotto no sekai (The world of feeling robots). Kōdansha. Picard, R. W. (1995). Affective Computing (Report No. 321; MIT Media Lab Oratory Perceptual Computing Section Technical Report). MIT. https://hd.media.mit.edu/tech-reports/TR-321.pdf Picard, R. W. (1997). Affective computing. MIT Press. Rhee, J. (2018). The robotic imaginary: The human and the price of dehumanized labor. University of Minnesota Press. Robertson, J. (2018). Robo sapiens japanicus: Robots, gender, family, and the Japanese nation. University of California Press. Roquet, P. (2020). Telepresence Enclosure: VR, Remote Work, and the Privatization of Presence in a Shrinking Japan. Media Theory, 4(1), 33–62. Shigemoto, Y. (2020). Meaning and Approach of New Product Designing Through Kansei Engineering. In G. Di Bucchianico, C. S. Shin, S. Shim, S. Fukuda, G. Montagna, & C. Carvalho (Eds.), Advances in Industrial Design (Vol. 1202, pp. 621–626). Springer International Publishing. https://do i.org/10.1007/978-3-030-51194-4_81 Shimizu, Y. (1996). Kansei kōgaku to sono yakuwari (Kansei engineering and its role). 47(2), 183–188. Shimizu, Y. (2016). Kansei kōgaku no hōhō (Methods of kansei engineering). Journal of the Society of Biomechanisms, 40(1), 5–11. https://doi.org/10.3951/sobim.40.1_5 Shinohara, A., Shimizu, Y., & Sakamoto, H. (1996). Kansei kōgaku e no shōtai (Invitation to kansei engineering). Morikita Shuppan. SoftBank. (2014, June 5). SoftBank mobile and Aldebaran unveil “Pepper”—The world’s first personal robot that reads emotions [Press]. SoftBank. https://www.softbank.jp/en/corp/group/sbm/news/pres s/2014/20140605_01/ Sony. (2021). Aibo. Sony. https://aibo.sony.jp The Science Council Japan. (2005). Gendai shakai ni okeru kansei kōgaku no yakuwari (The role of kansei engineering in contemporary society). Kansei Engineering Professional Committee, Division of Human and Engineering Partnership. Turing, A. M. (1950). Computing machinery and intelligence. In R. Epstein, G. Roberts, & G. Beber (Eds.), Parsing the Turing test: Computing machinery and intelligence (pp. 23–65). Springer. https://lin k.springer.com/chapter/10.1007/978-1-4020-6710-5_3 White, D. (2017). Affect: An Introduction. Cultural Anthropology, 32(2), 175–180. https://doi.org/10.1 4506/ca32.2.01 White, D., & Katsuno, H. (2021). Toward an Affective Sense of Life: Artificial Intelligence, Animacy, and Amusement at a Robot Pet Memorial Service in Japan. Cultural Anthropology, 36(2). https://doi.o rg/10.14506/ca36.2.03 Wilson, E. A. (2010). Affect and artificial intelligence. University of Washington Press. Yoshioka, M. (1996). Ningen kankaku keisoku ōyō gijutsu (Human sensory measurement application 16 Internet Policy Review 11(1) | 2022 technology). Journal of the Society of Fiber Science and Technology, Japan, 52(6), 242–246. in cooperation withPublished by 17 White, Katsuno